Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-04T19:38:53.034Z Has data issue: false hasContentIssue false

Financial shocks and inflation dynamics

Published online by Cambridge University Press:  11 November 2021

Angela Abbate
Affiliation:
Swiss National Bank
Sandra Eickmeier*
Affiliation:
Deutsche Bundesbank, CAMA, CEPR
Esteban Prieto
Affiliation:
Deutsche Bundesbank, IWH, CEPR
*
*Corresponding author. Email: [email protected].
Rights & Permissions [Opens in a new window]

Abstract

We assess the effects of financial shocks on inflation, and to what extent financial shocks can account for the “missing disinflation” during the Great Recession. We apply a Bayesian vector autoregressive model to US data and identify financial shocks through a combination of narrative and short-run sign restrictions. Our main finding is that contractionary financial shocks temporarily increase inflation. This result withstands a large battery of robustness checks. Negative financial shocks help therefore to explain why inflation did not drop more sharply in the aftermath of the financial crisis. Our analysis suggests that higher borrowing costs after negative financial shocks can account for the modest decrease in inflation after the financial crisis. A policy implication is that financial shocks act as supply-type shocks, moving output and inflation in opposite directions, thereby worsening the trade-off for a central bank with a dual mandate.

Type
Articles
Copyright
© The Author(s), 2021. Published by Cambridge University Press

1 Introduction

Despite a massive fall in demand during the Global Financial Crisis (GFC), the US economy experienced only a modest disinflation. In this paper, we provide evidence that inflation declined by less than many would have expected because the fall in demand was accompanied by a contractionary financial shock, which created inflationary pressures.Footnote 1

Our finding that a contractionary financial shock results in temporarily higher inflation provides useful empirical evidence to an ongoing theoretical debate about the response of inflation to financial shocks. Macroeconomic models featuring financial frictions are in fact compatible with financial shocks acting both as demand and cost-push shocks. This depends on whether aggregate demand or aggregate supply effects dominate in the transmission mechanism. Papers that model financial shocks as demand shocks include Curdia and Woodford (Reference Curdia and Woodford2010), Gertler and Karadi (Reference Gertler and Karadi2011), and Del Negro et al. (Reference Del Negro, Giannoni and Schorfheide2015). In these models, higher borrowing costs following contractionary financial shocks lead to a fall in consumption and investment. Both imply a decline in labor demand, which in turn reduces marginal costs and inflation. By contrast, papers that model financial shocks as cost-push shocks include Gilchrist et al. (Reference Gilchrist, Schoenle, Sim and ZakrajŠek2017), De Fiore and Tristani (Reference De Fiore and Tristani2013), Christiano et al. (Reference Christiano, Eichenbaum and Trabandt2014) and Meh and Moran (Reference Meh and Moran2010). In the first three papers, financial shocks influence the pricing decision of firms, either because firms prefer to hedge against the risk of relying on costly external finance, or because borrowing costs are actually part of firms’ marginal costs. An increase in the cost of external finance leads therefore to an increase in inflation, either via higher markups or via higher marginal costs. Table 1 summarizes the implications of these and other models for the behavior of inflation after financial shocks. Overall, financial shocks propagate through the economy through various aggregate supply- and demand-type channels, and their impact on inflation depends on which channel dominates. Footnote 2 Hence, whether financial shocks raise or lower aggregate inflation is ultimately an empirical question, which we try to answer in this paper.

Table 1. Theoretical responses of inflation to contractionary financial shocks implied by different models: An increase (decrease) is denoted with a + (-) sign. ft refers to De Fiore and Tristani (Reference De Fiore and Tristani2013); mm refers to Meh and Moran (Reference Meh and Moran2010); gnss refers to Gerali et al. (Reference Gerali, Neri, Sessa and Signoretti2010); gssz refers to Gilchrist et al. (Reference Gilchrist, Schoenle, Sim and ZakrajŠek2017); gk refers to Gertler and Karadi (Reference Gertler and Karadi2011); cw refers to Curdia and Woodford (Reference Curdia and Woodford2010); hh refers to Hristov and Huelsewig (Reference Hristov and Huelsewig2017); gln refers to Gelain et al. (Reference Gelain, Lansing and Natvik2017); cmr refers to Christiano et al. (Reference Christiano, Motto and Rostagno2010)

We apply a vector autoregressive model (VAR) estimated with Bayesian methods to a set of US macroeconomic and financial data. We identify financial shocks by imposing short-run sign restrictions on impulse response functions, as well as narrative restrictions reflecting common knowledge about the size and relevance of financial shocks during the GFC. Contractionary financial shocks in our model increase funding costs and decrease credit growth and stock prices, thereby matching the characterization of financial shocks in standard macroeconomic models. Most importantly, our identification strategy is fully agnostic about the response of inflation after financial shocks (and therefore about the contribution of financial shocks for inflation dynamics), while still disentangling financial from other structural shocks such as aggregate supply and demand shocks.

Our first key result is that contractionary financial shocks that decrease output and credit growth tend to increase inflation. The effect arises on impact and is significant over about a year. Based on a historical decomposition, we find that contractionary financial shocks contributed positively to inflation during and after the GFC. Absent financial shocks, the fall in inflation between 2008 and 2009 would have been on average 0.6 percentage points larger. Hence, negative financial shocks compensated to some extent deflationary pressures from other developments. Footnote 3 Toward the end of the sample, expansionary financial shocks prevented a more pronounced pick-up in inflation. Footnote 4

We then explore the transmission channels that can account for the inflation response after financial shocks. Contractionary financial shocks raise borrowing costs by increasing credit spreads. This can in turn increase overall marginal costs, which may partly explain the positive inflation response. Our result is therefore consistent with models where aggregate supply effects dominate in the transmission mechanism of financial shocks such as De Fiore and Tristani (Reference De Fiore and Tristani2013) and Gilchrist et al. (Reference Gilchrist, Schoenle, Sim and ZakrajŠek2017).

Our main finding is robust against a battery of checks. We show that financial shocks affect banking variables and survey measures of credit supply in the expected way, which supports our identification scheme. Moreover, our key result does not change if we exclude the GFC and post-GFC (zero lower bound) period from the estimation sample. Our key finding is therefore not driven by a special role of the GFC, nor by the limited ability of monetary policy to reduce the policy rate in response to contractionary financial shocks. In a counterfactual experiment, we further show that one could have predicted less of a decline in inflation in the aftermath of the GFC based on pre-GFC parameter estimates and correctly observed financial shocks during the GFC. We also show that the identified financial shock is not contaminated by other shocks including shocks to aggregate supply, demand, technology news, monetary policy, uncertainty, oil, and housing. Finally, we experiment with alternative measures of interest rates, inflation and credit, but our key finding remains unaffected.

Our approach offers two contributions relative to the existing literature. First, we propose an identification scheme for financial shocks that leaves the inflation response unrestricted. Previous work based on sign restrictions, which focuses on the effects of financial shocks on real economic activity, imposes either a positive comovement between output and inflation, or a positive comovement between output and the policy rate after financial shocks, which also biases the inflation response (Busch et al. (Reference Busch, Scharnagl and Scheithauer2010), Hristov et al. (Reference Hristov, Huelsewig and Wollmershaeuser2012), Conti et al. (Reference Conti, Neri and Nobili2015), De Santis and Darracq-Paries (Reference De Santis and Darracq-Paries2015), Furlanetto et al. (Reference Furlanetto, Ravazzolo and Sarferaz2017), Gambetti and Musso (Reference Gambetti and Musso2017)). Other papers restrict the impact effect on inflation and output to be zero, but leave the sign of their responses beyond impact unrestricted (Peersman (Reference Peersman2011), Gilchrist and ZakrajŠek (Reference Gilchrist and ZakrajŠek2012)). In these papers, contractionary financial shocks decrease inflation, though in Gilchrist and ZakrajŠek (Reference Gilchrist and ZakrajŠek2012), the response is not significant. Footnote 5 Also, only few papers have applied narrative restrictions so far, and we combine them with sign restrictions.

Our second contribution is to explore various transmission channels of financial shocks, and their implications for inflation in an aggregate time series setup. Previous work by Antoun de Almeida (Reference Antoun de Almeida2015), Balleer et al. (Reference Balleer, Hristov and Menno2015), and Gilchrist et al. (Reference Gilchrist, Schoenle, Sim and ZakrajŠek2017) relies on data at the product or firm level. These studies focus on the price-setting behavior of financially constrained firms relative to unconstrained ones, and show that borrowing constraints are an important determinant for price-setting behavior. While these studies are well suited to cleanly identify the effect of borrowing constraints on the price-setting behavior of firms, they lack clear-cut implications for the behavior of aggregate inflation after financial shocks. Footnote 6 Our study fills this gap by showing that contractionary financial shocks might increase aggregate inflation.

The results from our analysis have two policy implications. First, financial shocks which lower output and increase inflation and, hence, move these two variables in different directions, may worsen the trade-off faced by a central bank which seeks to stabilize both output and inflation. Second, a monetary policy designed to stimulate credit supply may have unintended disinflationary effects if it lowers borrowing costs and through that marginal costs. This would be undesirable in an environment where inflation is already low.

The rest of the paper is structured as follows. In Section 2, we provide details on data, methodology, and the financial shock identification strategy. We present results from the baseline model and investigate the transmission channels of financial shocks in Section 3. In Section 4, we discuss several robustness checks and explore the reaction of banking variables and survey measures to financial shocks. Section 5 concludes.

2 Data and modeling approach

2.1 Data

Our baseline analysis departs from an n-dimensional vector $X_{t}$ of seasonally adjusted quarterly series: real GDP, core inflation, a policy interest rate, credit growth, the EBP, and stock prices. These are standard variables in empirical macro-financial studies.

We use the core CPI, excluding energy and food, to measure inflation. We choose core, instead of headline inflation, in order to disentangle financial shocks from commodity price shocks, such as oil shocks. We further investigate this issue in Section 4 by including the oil price in our baseline VAR model. Footnote 7 The excess bond premium (EBP), which is taken from Simon Gilchrist’s website, is a risk premium that reflects systematic deviations in the pricing of US corporate bonds relative to the issuers’ expected default risk. It thus constitutes a proxy for the effective risk-bearing capacity of the financial sector, and is a direct price measure of credit supply. We measure credit growth using total credit to the private nonfinancial sector, taken from the Financial Accounts of the USA. In the robustness analysis, we also consider alternative credit measures. Nominal stock prices are taken from Robert Shiller’s website. We deflate the credit and stock price series by the CPI. Finally, we use the federal funds rate as the main policy interest rate. From 2008Q4 onwards, we replace the federal funds rate with the shadow short rate (SSR) from Krippner (Reference Krippner2015), which allows to account for unconventional monetary policy. The SSR reflects in fact changes in the term structure of interest rates, which is affected by quantitative easing and forward guidance policies (Gertler and Karadi (Reference Gertler and Karadi2015), Krippner (Reference Krippner2015)).

GDP and stock prices enter the model in logarithms, the policy rate and the EBP in levels, the CPI in year-on-year differences of logarithms. Footnote 8 We filter outstanding credit using log year-on-year differences. Financial intermediaries’ balance sheet variables are subject to secular trends due to changes in the structure of the financial system and regulatory changes. Following Adrian et al. (Reference Adrian, Moench and Shin2016), we deal with this issue by using detrended credit. Footnote 9 While the year-on-year change is a convenient way of detrending credit, we show below that our results are not affected when using a one-sided HP filter applied to outstanding credit. Footnote 10 If not stated otherwise, we take the series from the FRED database of the Federal Reserve Bank of St. Louis.

The sample period ranges from 1988Q1 to 2019Q2. By choosing 1988Q1 as a starting point, we exclude any monetary regime prior to the Greenspan one. Moreover, the period since the mid-1980s until the beginning of the GFC is associated with the Great Moderation. Hence, our sample period excludes the Great Inflation period. We show below that, even though our key finding is not affected when we extent the sample period backwards to 1973, magnitudes are somewhat different, which indicates parameter instability over a longer sample. Moreover, we check to what extent parameters have changed with the GFC in Section 4.

2.2 Vector autoregression

We assume that the $n \times 1$ vector of endogenous variables $\mathbf{y}_t$ follows a VAR model of order p:

(1) \begin{equation}\mathbf{y}_t^{\prime} = \mathbf{x}_t^{\prime}\mathbf{B} + \mathbf{w}_t^{\prime},\quad\mathbb{E}(\mathbf{w}_t^{\prime})=0,\,\mathbb{E}(\mathbf{w}_t\mathbf{w}_t^{\prime})=\mathbf{\Sigma},\end{equation}

where the regressors’ matrix is defined as $\mathbf{x}_t^{\prime} = [\mathbf{y}_{t-1}^{\prime}, \dots, \mathbf{y}_{t-p}^{\prime}, 1]$ and $\mathbf{B}$ is a $(np+1) \times n$ matrix of reduced-form coefficients. The $n\times 1$ vector $\mathbf{w}_t$ denotes reduced-form innovations which are assumed to be Gaussian with mean zero and positive definite covariance matrix $\mathbf{\Sigma}$ . The lag length p is set to 2, as suggested by the Akaike and Hannan–Quinn information criteria. We estimate the VAR using Bayesian methods. We specify diffuse/uninformative priors so that the information in the likelihood is dominant. These priors lead to a normal-(inverted-) Wishart posterior with mean and variance parameters corresponding to OLS estimates.

2.3 Identifying financial shocks

Identifying financial shocks in the data is not trivial. Different structural macroeconomic models with a financial sector have different implications for key features of financial shocks, including their effect on important variables such as inflation (cf. Table 1). One reason is the different transmission channels embedded in the model. Consequently, there is no consensus in the literature on how to best identify financial shocks.

To identify financial shocks, we propose a strategy that combines short-run sign restrictions on impulse response functions, and narrative restrictions on the shock size and the historical decomposition. These identifying restrictions are summarized in Table 2. Our identifying assumptions are economically reasonable and consistent with a large body of theoretical and empirical work on the dynamic propagation of financial shocks. At the same time, we remain fully agnostic about the effects of financial shocks on inflation dynamics. We discuss sign and narrative restrictions in turn.

Table 2. Restrictions to identify financial, aggregate demand and aggregate supply shocks: This table shows the restrictions imposed on the impulse response functions of the endogenous variables in the baseline VAR to identify financial, aggregate demand and aggregate supply shocks. All restrictions are imposed on impact and are implemented as $\geq 0$ ( $\uparrow$ ) or $\leq 0$ ( $\downarrow$ ). See text for details

Sign restrictions

Throughout the paper, we focus on contractionary financial shocks which lead to a decrease in GDP. Furthermore, we restrict the EBP to increase and credit growth and stock prices to fall. Footnote 11 These three restrictions are standard, and match key features of financial shocks in structural macroeconomic models.

We disentangle financial shocks from aggregate supply and aggregate demand shocks by assuming that contractionary financial shocks decrease credit growth by more than GDP. This relative restriction on credit growth and GDP is intuitively plausible. After contractionary aggregate supply and demand shocks, investors might increase risk premia and reduce credit in response to the economic downturn. However, it is reasonable to assume that investors adjust their lending and investment strategies only gradually in response to the shock. Hence, we should first observe a deterioration in economic activity, while credit growth should respond initially only mildly. Financial shocks, by contrast, originate in the financial sector (as seen in the GFC) and exogenously decrease credit supply. Lower credit supply, however, should transmit to the overall economy initially only weakly: entrepreneurs and households need some to time to fully adjust their production processes and demand decisions in response to the lower supply of credit. Hence, we should observe a fall in credit growth that exceeds the fall in output growth for some time. Note that this is similar to the intuition underlying the use of a zero contemporaneous restriction on output. However, our relative restriction is more agnostic than such a recursiveness assumption, which other empirical studies employ.

The relative restriction on credit growth and GDP is consistent with DSGE models featuring an active financial sector and shocks that directly affect banks’ balance sheets. Examples of these models include Covas and Driscoll (Reference Covas and Driscoll2013), Curdia and Woodford (Reference Curdia and Woodford2010), Gelain et al. (Reference Gelain, Lansing and Natvik2017), Gerali et al. (Reference Gerali, Neri, Sessa and Signoretti2010), Gertler and Karadi (Reference Gertler and Karadi2011), Hristov and Huelsewig (Reference Hristov and Huelsewig2017), Iacoviello (Reference Iacoviello2015), Meh and Moran (Reference Meh and Moran2010), and Villa (Reference Villa2016). Footnote 12 Key to this credit-to-GDP response is a realistic banking sector that is subject to endogenous bank balance-sheet constraints due to financial frictions. By contrast, standard financial accelerator models along the lines of Bernanke et al. (Reference Bernanke, Gertler, Gilchrist, Taylor and Woodford1999) do not necessarily imply that credit relative to GDP decreases after a contractionary financial shock (see, e.g. Christiano et al. (Reference Christiano, Motto and Rostagno2010), De Fiore and Tristani (Reference De Fiore and Tristani2013)). Footnote 13 The reason is that, in these models, firms effectively borrow directly from households while financial intermediaries are simply a veil. Given that we are interested in shocks and dynamics similar to those observed during the GFC—when the financial sector has been at the center stage—we choose to focus on restrictions consistent with models featuring a more complex financial sector.

Within the set of empirical studies, Eickmeier and Ng (Reference Eickmeier and Ng2015) impose the same restriction on the credit-to-GDP ratio as we do. Furthermore, our credit-to-GDP restriction is also consistent with empirical findings by Fornari and Stracca (Reference Fornari and Stracca2012) and De Santis and Darracq-Paries (Reference De Santis and Darracq-Paries2015), who have credit and GDP included in their model and identify financial shocks with sign restrictions, but leave the credit-to-GDP response unrestricted.

While we need the credit-to-GDP restriction to ex ante disentangle financial shocks from other shocks (especially when we re-estimate the model over the pre-GFC sample), this restriction is not crucial for our key findings. When the restriction is dropped, credit relative to GDP still declines after contractionary financial shocks, and our key results remains unchanged. Footnote 14

Finally, we disentangle financial shocks from monetary policy shocks by using a conditional sign restriction. Footnote 15 Specifically, we impose the restrictions described above, and then check the inflation response for each model which yields financial shocks satisfying the restrictions. If inflation falls on impact, the response of a central bank following a Taylor rule is unambiguously negative. In this case, we restrict the policy rate to decrease on impact. If inflation increases, we do not restrict the policy rate. In this way, we disentangle financial from monetary policy shocks. In standard New Keynesian and structural VAR models, monetary policy shocks move the interest rate in one direction, and the price level and output in the other direction (e.g. Peersman (Reference Peersman2005)). Given that we use the SSR, which translates (at least parts of) unconventional monetary policy measures into short-term interest rate movements, we apply the identification scheme to the full sample estimate of the model without distinguishing between non-ZLB and ZLB periods. In Section 4, we check whether this is valid by re-estimating the model over the pre-GFC period, by more explicitly accounting for monetary policy shocks, and by discussing the relationship between financial and monetary policy shocks. Footnote 16

We note that similar restrictions have been used in previous empirical work, which focuses on the real effect of financial shocks. However, this literature either restricts inflation to be positive or not to react on impact, or it restricts the policy rate to increase after expansionary financial shocks, which might also bias the inflation response. Footnote 17 The novelty of our identification scheme is that it disentangles financial shocks from other structural shocks, while being fully agnostic on the response of inflation and of the policy rate.

Paustian (Reference Paustian2007) shows that one can sharpen the shock identification by identifying additional shocks. Hence, in addition to financial shocks, we also explicitly identify two key standard macroeconomic shocks which are typically found to be important drivers of the economy: aggregate supply and demand shocks. Contractionary aggregate demand shocks lead to a decline in real GDP, credit, inflation and the policy rate, and raise the credit-to-GDP ratio. This last restriction disentangles financial and aggregate demand shocks, and is for instance consistent with the effects of a preference shock in Iacoviello (Reference Iacoviello2015), Hristov and Huelsewig (Reference Hristov and Huelsewig2017) and Gelain et al. (Reference Gelain, Lansing and Natvik2017), or with the effects of a fiscal shock in Hristov and Huelsewig (Reference Hristov and Huelsewig2017). Contractionary aggregate supply shocks lead to a decline in GDP and stock prices, raise the credit-to-GDP ratio, and lead to a higher inflation.

Finally, we restrict the remaining $n-3=3$ shocks not to have the same characteristics as the identified shocks. Hence, the identified financial, aggregate demand and aggregate supply shocks are the only shocks that satisfy the restrictions. We impose all sign restrictions only on impact, and hence remain relatively agnostic.

Narrative restrictions

Beside sign restrictions, we employ two narrative restrictions in our identification scheme. These are based on the assumption, now widely accepted, that financial shocks were extremely important during the GFC. Footnote 18 The first narrative restriction is imposed on the financial shock series: we impose that the largest contractionary financial shock over our sample period occurs in 2008Q3. The second narrative restriction is imposed on the historical decomposition: the absolute value of the contribution of the financial shock to the dynamics of the EBP in 2008Q3 is larger than the sum of the absolute value of the contributions of all other structural shocks. These restrictions deserve some discussion.

Our approach closely follows Ludvigson et al. (Reference Ludvigson, Ma and Ng2020) and Reference Ludvigson, Ma and NgLudvigson et al. (forthcoming) who impose restrictions on shocks, as well as AntolÍn-DÍaz and Rubio-RamÍrez (Reference AntolÍn-DÍaz and Rubio-RamÍrez2018) who restrain the historical decomposition. We impose one restriction on the shock and one on the historical decomposition, and hence exploit knowledge about realizations of financial shocks over time, and in comparison with realizations of other shocks in 2008Q3.

We choose 2008Q3 because it includes the month (September) when Lehman Brothers filed for bankruptcy, which is commonly associated as the major trigger of the Great Recession (see also Reference Ludvigson, Ma and NgLudvigson et al. (forthcoming)). While there had been financial turmoil before that date, with the burst of the subprime mortgage bubble in 2007, it is widely agreed that the largest shock hit the US economy (and probably also the rest of the world) in 2008Q3, with the collapse of Lehman Brothers in September 2008. The government had previously bailed out other major financial institutions (Bear Sterns, Fannie Mae, and Freddie Mac), and it came as a surprise that it decided not to rescue Lehman Brothers as well. This triggered financial market panic: stock prices tanked, and investors fled money market mutual funds. The shock spread through 2008Q4 and subsequent quarters. By 2008Q4, policy had already reacted: the US Congress passed a bailout bill in October 2008 and central banks tried to restore liquidity, for example, by directly issuing short-term loans for businesses, lowering interest rates to basically zero, and coordinating actions of central banks throughout the globe. Our timing choice is in line with Reference Ludvigson, Ma and NgLudvigson et al. (forthcoming) who focus on uncertainty shocks and find evidence of a large positive financial uncertainty shock in September 2008. It is also not inconsistent with Ludvigson et al. (Reference Ludvigson, Ma and Ng2020) who, in one of their applications, restrict the EBP credit spread shock to be particularly large either in September 2008 or in October 2008 or in both. In our view 2008Q4 was mostly characterized by the reaction to the Lehman shock, rather than by another financial shock, which is why we focus on 2008Q3 here. We have also checked whether any other shock (demand or supply shocks as well as the three other rotated orthogonalized residuals) has its extremum in 2008Q3 and found this not to be the case. Hence, financial shocks are the only shocks that have their extremum in 2008Q3.

Beside imposing that a particularly large contractionary financial shock occurred in 2008Q3, we also impose that the contribution of this shock to the EBP is very large. Given that the EBP is constructed as systematic deviations in the pricing of US corporate bonds relative to the issuers’ expected default risk, it should be relatively exogenous with respect to the rest of the economy, and it is plausible to assume an “overwhelming” contribution of financial shocks (as AntolÍn-DÍaz and Rubio-RamÍrez (Reference AntolÍn-DÍaz and Rubio-RamÍrez2018) put it) to the EBP in 2008Q3. To what extent the EBP is really largely exogenous to the rest of the economy is somewhat controversial in the literature. On the one hand, Furlanetto et al. (Reference Furlanetto, Ravazzolo and Sarferaz2017) find that credit shocks identified with sign restrictions explain 44% of the EBP’s forecast error variance at the 1-quarter horizon, and only 16% at the 20-quarter horizon. Gertler and Karadi (Reference Gertler and Karadi2015) find, moreover, a significant reaction of the EBP to monetary policy shocks. On the other hand, Gilchrist and ZakrajŠek (Reference Gilchrist and ZakrajŠek2012) recursively identify financial shocks and find that they explain a very large share of the EBP (median estimate: over 80%). Moreover, Caldara et al. (Reference Caldara, Fuentes-Albero, Gilchrist and ZakrajŠek2016) impose that financial shocks maximize the response of the EBP over a predefined horizon. We are more agnostic than Caldara et al. (Reference Caldara, Fuentes-Albero, Gilchrist and ZakrajŠek2016) and impose that financial shocks explain a large fraction of the EBP only in 2008Q3. We will show that there is room left for other shocks to explain EBP dynamics during the Great Recession.

In Section 4, we explore the robustness of our key results against alternative identification schemes. Importantly, while our baseline model and identification scheme allow us to disentangle financial from standard (macro and conventional monetary policy) shocks, we modify the model to explicitly separate financial shocks from other shocks such as technology news shocks or oil shocks. We also illustrate the effects of imposing the narrative restrictions in addition to the sign restrictions.

To implement the restrictions, we follow the algorithm in AntolÍn-DÍaz and Rubio-RamÍrez (Reference AntolÍn-DÍaz and Rubio-RamÍrez2018), described in Appendix B. We obtain 1000 draws from the posterior of the reduced-form coefficients that satisfy the baseline restrictions. We approximate their weights in the importance step by using 1000 draws.

3 Results

3.1 Results from the baseline model

Impulse response analysis

In Figure 1, we present impulse responses to a contractionary financial shock. The solid red lines are median impulse response. The shaded areas are 68% credible sets. Footnote 19 As imposed by the restrictions, the EBP increases and credit growth as well as the stock price decrease. The negative effects on credit growth, GDP, and the stock price are very persistent, with the peak effects occurring after 1–2 years following the shock. The interest rate falls, in line with the fall in GDP.

Figure 1. Impulse responses to a one-standard deviation contractionary financial shock: Plot shows median impulse responses (solid lines) and 68% credible sets (shaded areas). The responses of GDP and the stock price are in percent; those of the other variables are in percentage points.

Most importantly, inflation increases on impact by 0.1 percentage points and remains positive for around 1 year after the shock. We re-emphasize here that we are agnostic about the response of inflation after the financial shock, leaving its sign unrestricted. Hence, our result suggests that aggregate supply effects dominate demand effects in the transmission of financial shocks.Footnote 20

Variance and historical decomposition

Table 3 shows the forecast error variance decomposition for selected variables at the 4-year horizon. The financial shock accounts for more than half of the forecast error variance of credit growth, and for 45% of that of the EBP. Moreover, financial shocks explain 44% of GDP fluctuations, and 15% of inflation fluctuations. Footnote 21 Hence, on average over the sample period, financial shocks made notable, although not very large contributions to inflation dynamics.

Table 3. Forecast error variance decomposition: This table shows the median forecast error variance shares explained by financial shocks, for selected variables in the baseline VAR, at a 4-year horizon

Figure 2 shows the historical decomposition of the EBP, credit growth, inflation, and GDP over the period 1999–2019. Expansionary financial shocks contributed to the pre-crisis credit and output boom by holding the EBP down over most of this period and pushing credit growth and GDP up. Contractionary financial shocks over the GFC accounted for large parts of the drop in credit growth and, as imposed, the rise in the EBP. Consistent with Furlanetto et al. (Reference Furlanetto, Ravazzolo and Sarferaz2017) and Mumtaz et al. (Reference Mumtaz, Pinter and Theodoridis2018), the financial shock does not, however, fully explain the increase in the EBP, suggesting that the EBP is also affected by other shocks. Most importantly, contractionary financial shocks made positive contributions to inflation around and after the GFC. Between 2008 and 2009, contractionary financial shocks increased core inflation by around 0.6 percentage points. Put differently, core CPI inflation fell from 2.5% to 1.5% in 2008–2009, which is the period typically associated with the “missing disinflation puzzle”, Footnote 22 and our results suggest that the fall in inflation would have been 0.6 percentage points larger in the absence of financial shocks. Financial shocks also explain a substantial fraction of the GDP decline after the 2007–2009 recession. Our results for the GFC corroborate the results in Del Negro et al. (Reference Del Negro, Giannoni and Schorfheide2015). They show that a DSGE model needs to be augmented with financial frictions and a credit spread to successfully predict the moderate decline in inflation and the strong decline in output during the GFC.

Figure 2. Historical decomposition of key variables: Plot shows the contribution of all shocks to explaining the deviation of key variables from their deterministic component (lines) and the contribution of the financial shocks (bars). Contributions to GDP are in percent; those to the other variables are in percentage points.

We also assess the contribution of our other two identified, aggregate demand and aggregate supply, shocks, in order to understand what has brought inflation down (even though not by too much). We find that the initial decline of inflation, until 2009, is almost entirely explained by negative demand shocks. From 2010 onwards, negative demand shocks and positive supply shocks account for about a third of the decline in inflation each. The remaining third is explained by the unidentified shocks.

Lastly, we note that contractionary financial shocks also accounted for the financial headwinds in the late 1980s/early 1990s, and kept inflation somewhat up over much of that period as well (not shown). Moreover, expansionary financial shocks held down inflation since late 2016, and can therefore partly explain the “missing inflation” at the end of the sample.

Transmission channels of financial shocks relevant for inflation

Why does inflation increase after contractionary financial shocks? In Figure 3, we present impulse responses to the financial shock of variables that capture key transmission channels. We include these variables one by one in the baseline VAR. We treat the endogenous variables from our baseline model as block exogenous for the added variable, and restrict the residual covariance matrix so that shocks to the additional variables do not affect the endogenous variables nor the financial shock estimates.

Figure 3. Transmission channels of financial shocks relevant for inflation: Plot shows median impulse responses (lines) and 68% credible sets (shaded areas). Responses of investment, consumption, employment, and the real wage are in percent; those of the remaining variables are in percentage points. See text for a description of the variables.

The first row of Figure 3 shows that contractionary financial shocks lead to a strong and hump-shaped fall in real private investment, consumption and employment. The fall in consumption is potentially the result of wealth effects following the decline in asset prices after the financial shock. Investment decreases by more than consumption, consistent with the restriction imposed by Furlanetto et al. (Reference Furlanetto, Ravazzolo and Sarferaz2017) to identify financial shocks. The gradual decrease in demand is likely to be the reason behind the return of inflation to baseline after its initial increase.

The key determinant of firms’ pricing decisions are current and expected future marginal costs. We therefore explore their behavior by looking at the response of the labor income share, a proxy for marginal costs used for instance in Nekarda and Ramey (Reference Nekarda and Ramey2013). Figure 2 shows that the labor share does not move significantly in the first few quarters after the shock, and decreases afterwards as both real wages and employment decrease. Hence, the movement in the labor share cannot explain the early increase in inflation. However, it potentially contributes to the decline in inflation 1 year after the shock.

Models where firms have to borrow in advance to finance production, that is featuring a cost, credit, or working capital channel such as De Fiore and Tristani (Reference De Fiore and Tristani2013) and Christiano et al. (Reference Christiano, Eichenbaum and Trabandt2014), imply that financing costs are part of firms’ marginal costs and therefore matter for price-setting decisions. Footnote 23 Accordingly, we assess the responses of different borrowing spreads after the financial shock, capturing different segments of financial markets. The spreads are defined as the Baa corporate bond yield over the 10-year government bond yield; the 3-month commercial paper rate over the 3-month T-bill rate; the C&I loan rate over the 2-year government bond yield; the 30-year mortgage rate over the 10-year government bond yield. Footnote 24 All spreads increase significantly over the first few quarters, and the shapes of the responses roughly match the shape of the inflation response.

To summarize, results suggest that a mechanism related to the credit or working capital channel might in part be able to explain the increase in inflation after contractionary financial shocks. These shocks raise borrowing costs on a broad scale, causing an overall increase in firms’ marginal costs, which may contribute to the increase in inflation. Nevertheless, other supply-type mechanisms, such as the one emphasized in Gilchrist et al. (Reference Gilchrist, Schoenle, Sim and ZakrajŠek2017), may have played a role as well.

4 Validation and robustness

4.1 Responses of credit supply and banking variables to the financial shock

To better understand the characteristics of our financial shock, and to validate that we are indeed identifying a financial shock, we explore the behavior of credit supply measures and banking variables. The series are again added one by one to the VAR, treating the endogenous variables from our baseline model as block exogenous for the added variable. We present the results in Figure 4.

Figure 4. Response of credit supply and banking variables to a one-standard deviation contractionary financial shock: Plot shows median impulse responses (lines) and 68% credible sets (shaded areas). Responses of the willingness to lend to consumers, lending standards, the bank return on assets, the nonperforming loans ratio and the bank capital ratio in percentage points. See text for a description of the variables.

First, we add survey measures of credit supply in order to verify whether our identified shock is consistent with the answers of banking sector survey participants. We use data from the Senior Loan Officer Opinion Survey on Bank Lending Practices: the net percentage of domestic respondents tightening standards for C&I loans (“tightening standards”) and the net percentage of domestic respondents reporting increased willingness to make consumer installment loans (“willingness to lend to consumers”). Footnote 25 The willingness to lend to consumers decreases on impact after the contractionary financial shock, and remains negative for 1 year. Similarly, banks tighten their credit standards markedly on impact, and keep them above baseline for more than 1 year. Hence, both survey measures move in the expected direction.

Second, we include data on the return on assets of commercial banks, the ratio of nonperforming loans to total loans, the ratio of bank capital to total assets and the volatility of bank stock prices. Bank return on assets, a measure of bank profitability, decreases significantly and remains negative for about 2 years. This decrease is reflected in the response of the bank capital ratio, which also drops significantly on impact and remains negative for almost a year. The lower profitability translates into lower net worth, for example, bank capital, thus generating a weaker capital position. The ratio of nonperforming loans to total loans also rises significantly. The response is hump shaped, reaching its maximum after more than a year. The behavior of the nonperforming loans ratio follows closely the movement of GDP. The fall in economic activity after the financial shock worsens the balance sheets of entrepreneurs and households, which in turn delays scheduled loan payments (higher nonperforming loans). In addition, the volatility of bank stock prices increases significantly after the financial shock.

Overall, the response of these credit supply and banking variables are consistent with a financial shock.

4.2 Is the Great Recession period different?

One potentially critical point is that the Great Recession period might be different from more “normal” times; for example, because financial frictions are larger in crisis times or because the policy rate was constrained at its zero lower bound. To address this point, we re-estimate the model over the pre-GFC period, that is over the period 1988Q1–2007Q2. Figure 5 (left panel) shows that the positive inflation response after a contractionary financial shock is slightly weaker in the pre-GFC subsample, but the credible sets overlap suggesting that differences are probably not significant. Footnote 26

Figure 5. Does the GFC make a difference? Left graph: Impulse response functions of inflation to contractionary financial shocks, median baseline estimate (red solid line), and 68% credible sets (shaded areas), and pre-GFC estimate (blue dotted lines). Right graph: Historical decomposition of inflation, baseline model (all shocks: black solid line, financial shocks: red bars), and counterfactual contributions of financial shocks (based on structural shocks estimated over the full sample and reduced-form parameters estimated over the pre-GFC sample: blue dotted line). See text for details.

We also conduct a counterfactual experiment as follows. Footnote 27 We combine the structural shocks from the full model and the reduced-form parameters from the pre-GFC model in order to assess whether one could have predicted positive contributions of financial shocks (assuming the shocks had been observed) to inflation dynamics after the GFC. The answer is yes, even though the counterfactual contributions of financial shocks are slightly smaller compared to those resulting from the baseline model (blue dotted line in Figure 5, right panel). In particular, using the parameter estimates from the pre-GFC subsample, we find that the fall in inflation between 2008 and 2009 would have been 0.4 percentage points larger absent the negative financial shocks.

Overall, we can conclude that our main finding that contractionary financial shocks increase inflation also holds for less turbulent times. It is interesting to note that this key finding cannot be explained by the zero lower bound and monetary policy not being able to respond to negative financial shocks by lowering the interest rate. Also, based on pre-crisis parameters and assuming that financial shocks had been observed, one could have predicted that inflation would not fall as much as implied by a simple Phillips curve relationship. This corroborates the extended Phillips curve estimation results presented in the Appendix.

4.3 Understanding the role of the narrative restrictions

We assess next the importance of narrative restrictions by removing them from the set of identifying restrictions. Figure 6 compares the baseline impulse responses with those obtained from a specification without the two narrative restrictions (blue lines). When narrative restrictions are dropped, our key result remains the same: inflation increases temporarily after contractionary financial shocks. However, the responses of the EBP, GDP and credit growth to financial shocks are slightly weaker. Moreover, inference for EBP and inflation dynamics after the financial shock worsens. Figure 7 shows the corresponding historical decomposition. When the narrative restrictions are dropped, the contribution of financial shocks to the EBP, credit growth and GDP during the GFC is much smaller. In addition, the contribution to inflation is also smaller, both during and after the GFC.

Figure 6. Impulse responses to a one-standard deviation contractionary financial shock—role of the narrative restrictions: Plot shows median impulse responses and 68% credible sets in the baseline model (solid red lines and shaded areas), and in the model without narrative restrictions (solid and dotted blue lines). The response of GDP is in percent; those of the other variables are in percentage points.

Figure 7. Historical decomposition of key variables—effects of the narrative restrictions: Plot shows contribution of all shocks to explaining the deviation of key variables from their deterministic component in the baseline model (solid black lines), contribution of financial shocks in the baseline (red bars) and in the model without narrative restrictions (blue dotted lines). Contributions to GDP are in percent; those to the other variables are in percentage points.

Figures C2–C4 in the Appendix further show the effects of removing the narrative restrictions on either the shocks or the historical decomposition. The figures reveal that the restriction on the shock size improves inference for inflation, while the restriction on the historical decomposition leads to more plausible contributions of the financial shock during the GFC. Footnote 28

4.4 (Unconventional) monetary policy shocks

In Section 2.3, we have argued that our identification scheme disentangles financial shocks from monetary policy shocks typically identified in the literature. Recall that monetary policy shocks move interest rates in one direction, and prices and output in opposite directions. By contrast, our financial shocks either move output, inflation and the policy rate in the same direction or move output and inflation in opposite directions.

However, the aggregate supply channels that may influence the transmission of financial shocks could also play a role in the transmission of monetary policy shocks. The cost channel of monetary policy is one specific example of a transmission channel that could cause inflation to fall after expansionary monetary policy shocks. The cost channel of monetary policy has been in fact brought forward as one explanation of the “price puzzle”, for instance, by Castelnuovo (Reference Castelnuovo2012) and Gaiotti and Secchi (Reference Gaiotti and Secchi2006a). If the cost channel is a relevant transmission mechanism, our financial shocks may not be disentangled from monetary policy shocks. Put differently, a monetary policy shock that affects funding costs is a specific type of financial shocks.

This concern should be even more relevant for unconventional monetary policy, which is at least in part designed to explicitly stimulate credit supply and lower funding costs and, through that, to stimulate economic activity and inflation. Footnote 29 Existing work already emphasizes the link between unconventional monetary policy and credit supply or financial shocks. De Santis and Darracq-Paries (Reference De Santis and Darracq-Paries2015) explicitly identify unconventional monetary policy shocks in the euro area in a VAR model as shocks that increase credit supply. Moreover, in a time-varying parameter VAR for the USA, Prieto et al. (Reference Prieto, Eickmeier and Marcellino2016a) find that credit spread shocks have contributed positively to GDP growth from 2010 onwards. They argue that those credit spread shocks probably capture unconventional monetary policy actions.

We investigate here to what extent monetary policy actions lie behind our financial shocks. Figure 7 plots our financial shock estimates (solid line) against the monetary “policy news shock” measure taken from Nakamura and Steinsson (Reference Nakamura and Steinsson2018) (dashed line) in the last two decades of the sample. Footnote 30 This “policy news shock” is based on high-frequency responses of current and expected future interest rates in a 30-minute window surrounding scheduled Federal Reserve announcements, and captures the effects of forward guidance. Footnote 31 We normalize the shock series in Figure 8 so that negative values represent a monetary policy easing (which can be expected to raise credit growth and lower funding costs) and expansionary financial conditions and that the standard deviation is one over the period over which we plot the shocks.

Figure 8. Financial and monetary policy shocks: Identified financial shocks (solid line) and Nakamura and Steinsson (Reference Nakamura and Steinsson2018) monetary policy shocks (dashed line). The shocks are standardized so that negative values represent expansionary monetary policy and financial shocks. Both series have standard deviation of 1 over the considered sample. See text for details.

Figure 8 reveals that the two shocks are basically uncorrelated over the sample (correlation coefficient: $-0.03$ ). Financial shocks seem, however, somewhat correlated with monetary policy shocks around the 2001 recession and the GFC. In both episodes, the Federal Reserve strongly lowered the federal funds rate and provided liquidity to financial institutions in order to stabilize the financial system. Footnote 32 In addition, in the course of the GFC the Federal Reserve lent directly to borrowers and investors in credit markets, and purchased longer-term securities. Footnote 33 This finding is consistent with Eickmeier et al. (Reference Eickmeier, Metiu and Prieto2016) who find a price puzzle after an expansionary monetary policy shock in high-volatility periods. Similarly, Fry-McKibbin and Zheng (Reference Fry-McKibbin and Zheng2016) find that prices decline after expansionary monetary policy shocks in financial stress periods, but not in low stress periods. They argue that this is consistent with cost channel effects during financial stress periods.

We conduct two further checks. First, we include the Nakamura and Steinsson (Reference Nakamura and Steinsson2018) monetary policy shock as an explanatory variable in the VAR, and rerun the estimation and shock identification. Footnote 34 The idea is that the monetary policy shock should capture all remaining uncontrolled effects of monetary policy shocks. Second, we identify a monetary policy shock in addition to the baseline shocks, by imposing that the policy rate increases and inflation, output and credit growth decrease on impact after a contractionary monetary policy shock. Figure 13 in the Appendix shows that, even after explicitly accounting for monetary policy shocks, the contractionary financial shock continues to have inflationary effects.

To sum up, results suggest that our financial shock is barely contaminated by monetary policy shocks, and that contractionary financial shocks cleaned from monetary policy shocks still produce positive effects on inflation. We find, if anything, some correlation between financial and monetary policy shocks in financial stress periods. One implication is that a monetary policy measure targeting credit supply and borrowing costs may act as an expansionary financial shock and thus exert downward pressure on inflation through the mechanisms discussed above. This would not be desirable in an environment of already low inflation. However, our results do not imply that the overall effects of expansionary monetary policy on inflation are negative. Although expansionary monetary policy might put a downward pressure on inflation through credit supply effects, demand-side effects leading to an increase inflation could predominate. Analyzing those would go beyond the scope of this paper.

4.5 Further robustness checks: other shocks and alternative measures of interest rates, inflation, and credit

We carry out a large battery of further robustness checks. We test whether we disentangle financial from other shocks including housing shocks, technology news shocks, oil shocks, and uncertainty shocks. This is achieved by adding additional variables one by one to the baseline model, and imposing additional restrictions to identify one additional shock at a time, as discussed in Appendix C. We note that, for computational reasons, it is not feasible to identify all shocks simultaneously. Moreover, we replace our measures of interest rates, inflation and credit with alternative measures and add inflation expectations to the model. Finally, we re-estimate the model over a longer sample period starting in 1973 and a monthly version of our model. None of these alterations changes our results, and we refer to Appendix C for details.

5 Conclusion

While there exists a large literature on the relationship between financial shocks and the real economy, the literature on the link between financial shocks and inflation dynamics is still in its infancy. However, understanding the link between financial and price stability is of key importance to monetary policy makers.

In this paper, we use a Bayesian VAR analysis and propose a novel identification scheme for financial shocks based on sign and narrative restrictions, which remains fully agnostic about the effects of financial shocks on inflation. Our main finding is that contractionary financial shocks tend to have temporary inflationary effects. We also show that this inflationary effect prevented a much stronger fall in inflation during the financial crisis. Hence, our results suggest that financial shocks might be an additional explanation for the “missing disinflation puzzle” during the financial crisis, as well as for the “missing inflation puzzle” over the subsequent recovery phase. A key implication of our finding is that financial shocks which simultaneously lower output and rise inflation might worsen the policy trade-off for a central bank which seeks to stabilize both.

We also explore different transmission channels of financial shocks and their implications for inflation dynamics. Our findings suggest that higher borrowing costs could explain the observed short-term inflationary effects of financial shocks.

Our key result that contractionary financial shocks have inflationary effects is robust against a battery of robustness checks, such as the use of alternative or additional variables, alternative shock identification schemes and variations of the sample period. Moreover, the identified financial shocks affect banking variables and survey measures of credit supply in the expected way. Finally, although our identified financial shock series is basically uncorrelated with the Nakamura and Steinsson (Reference Nakamura and Steinsson2018) monetary policy shock measure over the entire sample period, we detect some mild correlation between them in times of financial stress. One implication is that a monetary policy that stimulates credit supply and lowers funding costs may act as an expansionary financial shock with potentially disinflationary effects, which could be undesirable in a low inflation environment.

We note that we focus here on a broad financial (credit supply) shock. One promising future avenue could be to assess the effects of specific types of financial shocks, such as those emphasized in Table 1.

Acknowldgements

The authors would like to thank Benjamin Born, JÖrg Breitung, Dario Caldara, Todd Clark, Francesco Furlanetto, Gert Peersman, Harald Uhlig, and Fabrizio Venditti. We also thank participants of SNB and Bundesbank seminars, the annual research conference of the Bundesbank, the Norges Bank-Bundesbank workshop, a meeting of the Working Group of Econometric Modelling at the ECB (Frankfurt, May 2016), the Workshop on Empirical Macroeconomics (Ghent, May 2016), the Third Annual Conference of the International Association for Applied Econometrics (Milan, June 2016), the 22nd International Conference on Computing in Economics and Finance (Bordeaux, June 2016), and two anonymous referees for very helpful comments and discussions. Boele Bonthuis and Daniel Maas provided valuable research assistance. The opinions expressed in this paper are those of the authors and do not reflect the views of the Deutsche Bundesbank nor of the Swiss National Bank. Neither takes responsibility for any errors or omissions in, or for the correctness of, the information contained in this paper.

Conflicts of interest

None

Appendix A. Phillips curve estimation

We explore the role of financial conditions in explaining inflation dynamics by estimating a series of simple Phillips curves. We estimate the Phillips curves based on a pre-GFC sample from 1988Q1 to 2007Q4 and use the estimated coefficients to generate predicted inflation rates for the GFC and the subsequent period. As a simple baseline against which to compare alternative specifications, we consider the following expectation-augmented Phillips curve:

\begin{equation*}\pi_t = c + \kappa x_t + \beta \pi_{t+1}^{e} + v_t,\end{equation*}

where $\pi_t$ is headline inflation, $\pi_{t+1}^{e}$ inflation expectations, $x_t$ a measure of economic activity, and $v_t$ a shock. We measure economic activity through the difference between the unemployment rate and the natural rate of unemployment, estimated by the Congressional Budget Office but assumed here to be observable. We consider two measures of inflation expectations: backward-looking, whereby expected inflation can be approximated by the average of the previous four quarters’ inflation rates as in Mazumder and Ball (Reference Mazumder and Ball2011), and forward-looking, whereby expected inflation is measured by one-year ahead inflation expectations taken from the University of Michigan consumer survey. Following Coibion & Gorodnichenko (Reference Coibion and Gorodnichenko2015), we estimate the Phillips curves using and instrumental variables (IV) approach, instrumenting the current unemployment gap through a constant and its lag.

Figure A1 shows actual inflation (blue thick solid line) along with the two Phillips-curve predictions, respectively, based on backward-looking and household inflation expectations (red dashed and solid black lines). We confirm here the findings of Coibion & Gorodnichenko (Reference Coibion and Gorodnichenko2015): A backward-looking Phillips curve, estimated over the pre-crisis sample period, substantially underpredicts consumer price inflation between 2009 and 2011 (the period they associate the “missing disinflation” puzzle with). This puzzle is partially solved when households’ forward-looking inflation expectations are added to the set of explanatory variables.

Oil prices have also been mentioned as a possible explanation for why inflation did not fall more after the GFC. To examine whether oil prices improve the fit of the Phillips curve, we augment the model based on household inflation expectations with oil price inflation. The resulting counterfactual inflation rate (green dashed line in Figure A1) indicates that oil prices improve the fit of the Phillips curve model, especially in the 2009–2010 period.

To gauge if financial conditions can improve the predictive ability of the Phillips Curve we augment the above specification with the excess bond premium (EBP), and estimate the following equation:

\begin{equation*}\pi_t = c + \kappa x_t + \beta E_t\pi_{t+1}^{e} + \delta \pi_t^{oil} + \gamma ebp_t + v_t,\end{equation*}

where $\pi_{t+1}^{e}$ are households inflation expectations. For consistency, we instrument the EBP with a constant and its lag. Table A1 (last column) presents the estimation results and shows that the coefficient of the EBP $\gamma$ is positive and significant. This indicates that a worsening of credit conditions raises inflation. This result is in line with De Fiore and Tristani (Reference De Fiore and Tristani2013), who find a sizeable positive coefficient on credit spreads in their estimated Phillips curve. The black dotted line in Figure A1 shows the counterfactual inflation rate produced by this Phillips curve specification augmented with financial conditions. During and after the GFC, the implied inflation rates are consistently above those from the model without the EBP. Hence, contractionary financial conditions do indeed help explain why inflation was not much lower during this period. Augmenting the Phillips curve with the EBP helps especially in 2009, where the predicted inflation rate is up to 1 percentage point higher compared to the model with only household expectations.

Figure A1. Inflation and Phillips curve predictions: Plot shows actual headline inflation (thick solid line), and predictions from different Phillips curve specifications, based on backward-looking inflation expectations (red dashed line), household inflation expectations (solid black line), household inflation expectations and oil price inflation (green dashed line), household inflation expectations, oil price inflation, and the excess bond premium (black dotted line). See Appendix A for further details.

Table A.1. Phillips curve estimation results: IV-estimation results for different Phillips curve specifications, with backward-looking inflation expectations (back) or household inflation expectations (hh), with or without the excess bond premium and oil price inflation. Slack is measured by the unemployment gap. Robust standard errors are in parentheses. See Appendix A for further details

Robust standard errors are in parentheses.

***p< 0.01, **p< 0.05, *p < 0.1

Overall, while the EBP’s contribution in predicting inflation may be modest, these results support the notion that tighter financial conditions might be associated with higher inflation rates and are in line with results from our structural approach.

Appendix B. Bayesian estimation

This section describes the algorithm that is used to identify sign and narrative sign restrictions, and relies heavily on AntolÍn-DÍaz and Rubio-RamÍrez (Reference AntolÍn-DÍaz and Rubio-RamÍrez2018).

We start with the orthogonal reduced-form parametrization of the reduced-form VAR in equation (1), as in Arias et al. (Reference Arias, Rubio-RamÍrez and Waggoner2018). This parametrization is characterized by the reduced-form parameters $\mathbf{B}$ and $\mathbf{\Sigma}$ , together with an orthogonal $n\times n$ matrix $\mathbf{Q} \in \textit{O}(n)$ .

We have chosen priors of ( $\mathbf{B}, \mathbf{\Sigma}, \mathbf{Q}$ ) that are uniform-normal-inverse-Wishart. The following algorithm makes independent draws from the uniform-normal-inverse-Wishart posterior of ( $\mathbf{B}, \mathbf{\Sigma}, \mathbf{Q}$ ) conditional on the traditional sign and narrative sign restrictions:

  1. (i) Independently draw ( $\mathbf{B}, \mathbf{\Sigma}$ ) from the normal-inverse-Wishart posterior of the reduced-form parameters and $\mathbf{Q}$ from the uniform distribution over $\textit{O}(n)$ .

  2. (ii) Check whether traditional sign restrictions and narrative sign restrictions are satisfied.

  3. (iii) If not, discard the draw. If yes, compute the importance weight of ( $\mathbf{B}, \mathbf{\Sigma}, \mathbf{Q}$ ) as follows:

    1. (a) Simulate M independent draws of shocks $\varepsilon^{\nu}$ from the standard normal distribution, where $\nu$ is the number of shocks constrained by the narrative sign restrictions (1 in this application).

    2. (b) Approximate $\omega(\mathbf{B}, \mathbf{\Sigma}, \mathbf{Q})$ by the proportion of M draws (1000 in this application) the satisfy the narrative sign restrictions and set the importance weight to $1/\omega(\mathbf{B}, \mathbf{\Sigma}, \mathbf{Q}).$

  4. (iv) Return to step (i) until the required number of draws has been obtained (1000 in this application).

  5. (v) Draw with replacement from the set of ( $\mathbf{B}, \mathbf{\Sigma}, \mathbf{Q}$ ) using the importance weights.

Appendix C. Additional results and robustness

C.1 Effects of each of the narrative restrictions

C.2 Financial shocks versus other shocks not explicitly accounted for in the baseline model

Our baseline specification is a relatively small VAR model, which still allows us to separate financial shocks from standard shocks, such as aggregate demand and supply shocks. Here, we modify the baseline model and discuss to what extent our financial shocks are disentangled from other shocks not explicitly accounted for in our baseline model. It is computationally not feasible to account for all shocks simultaneously and, hence, add additional variables and identify additional shocks one by one. Figures C5 and C6 show impulse responses of inflation to financial shocks. We plot median impulse responses from the alternative models with median impulse responses and credible sets from the baseline model.

Figure C2. Impulse responses to a one-standard deviation contractionary financial shock—effects of the narrative restriction on the shock size: Robustness checks Ia: Plot shows median impulse responses and 68% credible sets in the baseline model (solid red lines and shaded areas), and in the model without narrative restriction (solid and dotted blue lines). The response of GDP is in percent; those of the other variables are in percentage points.

Figure C3. Impulse responses to a one-standard deviation contractionary financial shock—effects of the narrative restriction on the historical decomposition: Robustness checks Ib: Plot shows median impulse responses and 68% credible sets in the baseline model (solid red lines and shaded areas), and in the model without narrative restrictions (solid and dotted blue lines). The response of GDP is in percent; those of the other variables are in percentage points.

Figure C4. Historical decomposition of key variables—effects of each narrative restrictions: Robustness checks Ic: Plot shows contribution of all shocks to explaining the deviation of key variables from their deterministic component (solid black lines), contribution of financial shocks in the baseline model (red bars), in the model without shock size restriction (blue dotted lines), and in the model without restriction on historical decomposition (cyan dashed lines). Contributions to GDP are in percent; those to the other variables are in percentage points.

Figure C5. Effect of contractionary financial shocks on inflation: Robustness checks II: Plot shows median impulse responses (red solid line) and 68% credible sets (shaded areas) from the baseline model. Remaining lines show median impulse responses of inflation based on alternative models. Disentangle from housing shock: adding real estate value to credit to the baseline VAR and restricting it to rise on impact after contractionary financial shocks. Disentangle from tech and tech news shock: simultaneous identification of technology shocks, technology news shocks, financial shocks and aggregate demand shocks. Disentangle from oil shock: adding oil prices to the baseline VAR and restricting the oil price not move on impact after baseline shocks.

Figure C6. Effect of contractionary financial shocks on inflation: Robustness checks III: Plot shows median impulse responses (red solid line) and 68% credible sets (shaded areas) from the baseline model. Remaining lines show median impulse responses of inflation based on alternative models. Disentangle from uncertainty shock: adding uncertainty measure to the baseline VAR and restricting the EBP to rise by more than uncertainty after financial shocks. Following Furlanetto et al. (Reference Furlanetto, Ravazzolo and Sarferaz2017), we normalize uncertainty to have the same standard deviation as the EBP. Add Nakamura–Steinsson shock: adding the monetary policy shocks series of Nakamura and Steinsson (Reference Nakamura and Steinsson2018) to the baseline VAR. Identify a monetary policy shock: simultaneous identification of monetary policy, financial and aggregate demand and supply shocks.

Technology and technology news shocks

We add total factor productivity (TFP) to our model, which now contains $\tilde{n}=n+1$ endogenous variables. The TFP series is the one suggested by Fernald (Reference Fernald2012), adjusted for unobserved factor utilization, and it enters in logarithmic form. In addition to financial and aggregate demand shocks, we simultaneously identify technology shocks (instead of aggregate supply shocks) and technology news shocks.

We proceed as in Barsky and Sims (Reference Barsky and Sims2011). First, we identify the technology shock as the only one affecting TFP contemporaneously, that is via zero restrictions. Among the remaining $(\tilde{n}-1)$ shocks, the technology news shock explains the maximum of the forecast error variance of the TFP series, but has no contemporaneous impact on TFP. Among the remaining $(\tilde{n}-2)$ shocks, we identify financial and aggregate demand shocks as in the baseline model. As imposed by the identification strategy, TFP does not react on impact and does not move very significantly thereafter after financial shocks. This finding is not at odds with theory, which is ambiguous on the effects of financial shocks on productivity. Footnote 35 Figure 12 shows that the response of inflation to financial shocks is slightly weaker than in the baseline model, but the difference is probably not significant.

Housing shocks

Next, we consider a narrower financial (credit supply) shock, which we disentangle explicitly from a housing shock. Following Furlanetto et al. (Reference Furlanetto, Ravazzolo and Sarferaz2017), we add real estate value to credit (taken from the Financial Accounts) and restrict this ratio to rise on impact, that is credit declines by more than the real estate value, after contractionary financial shocks. Inflation rises by slightly more after financial shocks than in the baseline model (Figure C5).

Oil shocks

Oil price inflation is defined as the log year-on-year change in the West Texas Intermediate (WTI) price of crude oil. Footnote 36 In order to disentangle financial from oil shocks, we restrict the oil price not to move on impact after financial shocks. The zero restriction on the oil price implies that oil supply and oil demand, which should move the oil price on impact, react only with a delay to financial shocks. The inflation response is basically identical (Figure C5).

Uncertainty shocks

To disentangle financial shocks from uncertainty shocks, we add the uncertainty measure constructed by Jurado et al. (Reference Jurado, Ludvigson and Ng2015) (macroeconomic uncertainty, forecast horizon of 1) and restrict the EBP to rise by more than uncertainty on impact (see Furlanetto et al. Reference Furlanetto, Ravazzolo and Sarferaz2017). The resulting inflation reaction after the financial shock is very similar to the baseline response (Figure C6). Furthermore, macroeconomic uncertainty rises temporarily after the financial shock. It is theoretically unclear how uncertainty affects inflation, and it is hence unclear whether uncertainty represents an additional channel through which financial shocks affect inflation. On the one hand, a rise in uncertainty can lower inflation through standard aggregate demand effects associated with nominal rigidities (Leduc and Liu Reference Leduc and Liu2016). On the other hand, a rise in uncertainty can increase inflation in models with concave profit functions and price adjustment costs (FernÁndez-Villaverde et al. (Reference FernÁndez-Villaverde, GuerrÓn-Quintana, Kuester and Rubio-RamÍrez2015) and Mumtaz and Theodoridis Reference Mumtaz and Theodoridis2016).

Monetary policy shocks

C.3 Alternative inflation rates, interest rates, and credit measures

Control for an alternative measure of inflation and for inflation expectations

As a set of alternative checks, we replace core CPI inflation with core PPI inflation (Figure C7). Core PPI inflation increases by much more than core CPI inflation. A possible reason could be that PPI inflation is a more direct measure of the price-setting behavior of producers, and that the emphasized supply channels are especially relevant for producers.

We also control for inflation expectations as an omitted variable. Inflation expectations are measured as 1-year ahead forecasts of (year-on-year) inflation from the Surveys of Consumers conducted by the University of Michigan. This follows Castelnuovo and Surico (Reference Castelnuovo and Surico2010), who argue that inflation expectations should be included in monetary VARs as they help to identify structural shocks. Although the authors focus on monetary policy shocks, this problem might affect other shocks in the system as well. Footnote 37 Moreover, we would like to understand whether we are able to replicate previous Phillips curve findings, that is that financial conditions have an impact on inflation even when we control for inflation expectations. We do not impose any restriction on inflation expectations. The financial shock still produces an inflationary effect (Figure C7). Inflation expectations also rise temporarily after financial shocks (not shown).

Alternative measures of the short-term interest rate

We also check the sensitivity of our main results to changes in the short-term interest rate. First, we replace the linked federal funds rate SSR variable with the original federal funds rate. Second, following Gertler and Karadi (Reference Gertler and Karadi2015) we use the 2-year T-bill rate as the monetary policy indicator. Third, we link the federal funds rate with the SSR proposed by Wu & Xia (Reference Wu and Xia2016). Inflation rises after the financial shock regardless of the exact measure of the short-term interest rate (Figure C7).

Figure C7. Effect of contractionary financial shocks on inflation: Robustness checks IV: Plot shows median impulse responses (red solid line) and 68% credible sets (shaded areas) from the baseline model. Remaining lines show median impulse responses of inflation based on alternative models. Core ppi inflation: replacing core cpi inflation with core ppi inflation. Add inflation expectations: adding 1-year ahead inflation expectations to the baseline VAR. Federal funds rate: replacing linked federal funds rate SSR variable with the original federal funds rate series. Two-year rate: replacing linked federal funds rate SSR variable with the 2-year T-Bill rate. Ssr Wu-Xia: replacing SSR of Krippner (Reference Krippner2015) with SSR of Wu & Xia (Reference Wu and Xia2016).

Alternative credit and spread measures

We experiment with alternative measures of credit growth and credit spreads to check whether our findings are driven by the baseline credit measure and the EBP. We first replace our measure of total credit with either business credit, total bank credit, or commercial and industrial bank loans. Moreover, we use a different detrending method for credit. Rather than using year-on-year changes we employ a one-sided HP filter applied to the log of outstanding credit.

Furthermore, we replace the EBP with a composite business lending spread, computed as the weighted average of the commercial paper spread, the Baa spread and the C&I loan spread. Footnote 38 As for the EBP, we restrict the composite lending spread to decrease after the financial shock.

All identified contractionary financial shocks from these modified models turn out to be inflationary and very similar to those from the baseline model (Figure C8).

Figure C8. Effect of contractionary financial shocks on inflation: Robustness checks V: Plot shows median impulse responses (red solid line) and 68% credible sets (shaded areas) from the baseline model. Remaining lines show median impulse responses of inflation based on alternative models. Business credit: replacing total credit growth with business credit growth. Total bank credit: replacing total credit growth with total bank credit growth. C&i loans: replacing total credit growth with commercial and industrial bank loan growth. HP-filtered credit: replacing total credit growth with hp-filtered credit. Composite lending spread: replacing EBP with a composite lending spread.

C4. Long sample and monthly model

We finally re-estimate, as a robustness check, our model over a longer sample period, starting in 1973Q1, which marks the beginning of the availability of the EBP. Recall that we opted for the shorter baseline sample starting in 1988 in order not to mix up different monetary regimes. Estimating the model over the long sample is, nevertheless, a useful exercise, as it allows us to exploit more information and also indicates us whether parameters have changed over time. We find that inflation increases after a negative financial shock, confirming our baseline key result. The effect is, however, larger and more volatile, in line with the literature which examines the Great Moderation (e.g. Kim and Nelson (Reference Kim and Nelson1999), McConell and Perez-Quiros (Reference McConell and Perez-Quiros2000)). This is an indication for parameter instability over the long sample period and supports our more conservative choice for our baseline to start in 1988.

As a final check, we re-estimate a monthly version of our model (over our baseline sample period). We replace GDP with industrial production, which is available on a monthly basis. We use six lags. The identification scheme is comparable to the one in the baseline model. We impose sign restrictions on impact (but restricting the first 3 months does not change results), and the narrative restrictions on September 2008. The quarterly impulse response shown is the arithmetic mean over 3 months of the median of the monthly impulse response function. Again, we find a positive, but slightly smaller effect on inflation after the negative financial shock, which is statistically significant. That the effect is smaller may not be surprising, given that, unlike GDP, industrial production does not include the financial sector, which may be strongly involved when a financial shock hits the economy.

Overall, we conclude that our main finding is robust against these changes (Figure C9).

Figure C9. Effect of contractionary financial shocks on inflation: Robustness checks VI: Plot shows median impulse responses (red solid line) and 68% credible sets (shaded areas) from the baseline model. Remaining lines show median impulse responses of inflation based on alternative models. Long sample: re-estimating our model over 1973Q1–2019Q2. Monthly model: re-estimating a monthly model using industrial production instead of GDP.

Footnotes

1 Another prominent explanation for this “missing disinflation puzzle” is that inflation expectations remained well anchored in the aftermath of the crisis (Bernanke (Reference Bernanke2010a), Yellen (Reference Yellen2013), Coibion & Gorodnichenko (Reference Coibion and Gorodnichenko2015)), something we do not study explicitly here.

2 The models do not allow to associate specific characteristics of financial shocks to a specific inflation response. For example, Gerali et al. (Reference Gerali, Neri, Sessa and Signoretti2010), Gertler and Karadi (Reference Gertler and Karadi2011) and Hristov and Huelsewig (Reference Hristov and Huelsewig2017) consider shocks to bank capital, but inflation responses differ (cf. Table 1). It would be interesting to investigate empirically the effects of different types of financial shocks (such as shocks from the banking vs. the non-banking financial sector or shocks to different positions of the balance sheets of banks), but this goes beyond the scope of this paper and is left for future research.

3 Estimation of a reduced-form Phillips curve, augmented with the excess bond premium (EBP) confirms these results. See Appendix A.

4 The recovery after the GFC is characterized by the “missing inflation puzzle”, addressed for instance by Bobeica and Jarocinski (Reference Bobeica and Jarocinski2019) and Linde and Trabandt (Reference Linde and Trabandt2019).

5 As a cross-check, we have applied a recursive identification scheme similar to the one adopted in Gilchrist and ZakrajŠek (Reference Gilchrist and ZakrajŠek2012) and find, consistent with Gilchrist and ZakrajŠek (Reference Gilchrist and ZakrajŠek2012), a negative inflation response after contractionary financial shocks (which is however insignificant in Gilchrist and ZakrajŠek (Reference Gilchrist and ZakrajŠek2012)).

6 Gilchrist et al. (Reference Gilchrist, Schoenle, Sim and ZakrajŠek2017) point out representativeness of their data for aggregate fluctuations, but their data are limited to a sample of firms.

7 Oil shocks have been investigated in relation to the “missing disinflation puzzle”, see Coibion & Gorodnichenko (Reference Coibion and Gorodnichenko2015). They are not the focus of this paper, which is why we use core, not headline inflation. While most studies that investigate the puzzle use headline inflation, Van Zandweghe (Reference Van Zandweghe2019) also uses core inflation.

8 See Ang and Piazzesi (Reference Ang and Piazzesi2003), Bjoernland and Leitemo (Reference Bjoernland and Leitemo2009), Canova et al. (Reference Canova, Gambetti and Pappa2007) and Primiceri (Reference Primiceri2005) for similar transformations of key variables in VAR models.

9 Credit growth can also be interpreted as a proxy for new credit, which is a flow variable, as GDP (whereas credit is a stock variable). This makes the change in credit a better fit for the imposed sign restrictions on the response of credit relative to GDP, which we discuss later in the text. Moreover, a large share of US credit is long term and therefore largely predetermined. Hence, it is more meaningful to assume that new credit reacts rapidly to the shocks.

10 See Edge and Meisenzahl (Reference Edge and Meisenzahl2011) for a discussion on the consequences of different detrending methods for credit gap measures.

11 By restricting the stock price to decrease, we follow Furlanetto et al. (Reference Furlanetto, Ravazzolo and Sarferaz2017). They argue that investment-specific demand shocks lead to a negative co-movement between stock prices and output, while financial shocks imply a positive co-movement between the two.

12 While the credit-to-GDP restriction can be unambiguously derived from DSGE models with a banking sector with frictions, these models do not imply a clear-cut inflation response (see Table 1).

13 Carlstrom et al. (Reference Carlstrom, Fuerst, Ortiz and Paustian2014) and Carlstrom et al. (Reference Carlstrom, Fuerst and Paustian2016) extend the standard financial accelerator framework to include an optimal lending contract between lenders and borrowers. In this enhanced framework credit relative to GDP declines after a contractionary financial shock.

14 Results are not shown but are available upon request.

15 This approach is similar in spirit to Arias et al. (Reference Arias, Caldara and Rubio-RamÍrez2019) who restrict the systematic component of monetary policy.

16 Relaxing the conditional restriction on the policy rate produces the same qualitative results, but increases the model uncertainty associated with the sign restrictions. This might be because the financial shock is not appropriately separated from a monetary policy shock without the restriction on the interest rate.

18 See for instance Yellen (Reference Yellen2015) or Bernanke (Reference Bernanke2018), who argue that the unusual severity of the GFC was mainly due to panic in funding and securitization markets, which lead to a negative credit supply shock.

19 Our key results also hold with 90% credible sets.

20 To save space we show results for all variables included in our VAR only for the baseline, but not for the checks conducted later.

21 The share for GDP is larger than what is reported in the empirical linear VAR literature which finds contributions of credit supply or financial shocks to the forecast error variance of GDP between 10% and 30%. See, for example, Bean et al. (Reference Bean, Paustian, Penalver and Taylor2010), Busch et al. (Reference Busch, Scharnagl and Scheithauer2010), De NicolÒ and Lucchetta (Reference De NicolÒ and Lucchetta2011), Eickmeier and Ng (Reference Eickmeier and Ng2015), Furlanetto et al. (Reference Furlanetto, Ravazzolo and Sarferaz2017), Helbling et al. (Reference Helbling, Huidrom, Kose and Otrok2011a), Hristov et al. (Reference Hristov, Huelsewig and Wollmershaeuser2012), Meeks (Reference Meeks2012) and Peersman (Reference Peersman2011). We work here with a relatively small VAR compared to these other studies, which might be an explanation. Nonlinear VARs tend to find somewhat larger contributions of financial shocks in financial stress periods (up to 70%) than the linear VARs, see Lhuissier (Reference Lhuissier2017), Hubrich and Tetlow (Reference Hubrich and Tetlow2015), Abbate et al. (Reference Abbate, Eickmeier, Lemke and Marcellino2016). Our results, hence, lie within the range of all these studies.

22 While most of the literature associates the “missing disinflation puzzle” with the end of 2007–2009 or 2008–2009 (e.g. Hall (Reference Hall2011), Bobeica and Jarocinski (Reference Bobeica and Jarocinski2017), Van Zandweghe (Reference Van Zandweghe2019), Linde and Trabandt (Reference Linde and Trabandt2019)), Coibion & Gorodnichenko (Reference Coibion and Gorodnichenko2015) associate it with 2009–2011. We find positive contributions over most of 2008–2011.

23 Barth and Ramey (Reference Barth, Ramey, Bernanke and Rogoff2001) argue that the cost channel is potentially an important feature of the transmission mechanism of shocks to funding conditions. They show that the share of working capital in the nonfinancial corporate sector, defined as private inventories plus trade receivables, is large relative to total sales. Following their definition of (gross) working capital, we calculate that it amounts to more than five quarters of final sales over our sample period. This number is similar to the one computed by Barth and Ramey (Reference Barth, Ramey, Bernanke and Rogoff2001).

24 The USA has non-negligible mortgage prepayment activity. The conventional estimate for the duration of mortgages in the USA is 7–8 years. Hence, rather than computing the mortgage spread relative to the 30-year government bond rate, we calculate the spread relative to the 10-year government bond rate. See also Walentin (Reference Walentin2014) for a similar discussion.

25 We construct the “tightening standards” series as the arithmetic mean of the series for large, medium and small firms. Since the individual “tightening standards” series start in 1990Q2, we estimate the model over 1990Q2–2019Q2. The “willingness to lend to consumers” series is instead available over the entire sample period.

26 Note that the credible sets from the pre-GFC model are wider. This is not surprising given that, in the model estimated over the full sample, we impose narrative restrictions, which we show in the next subsection to sharpen inference.

27 We thank an anonymous referee for suggesting this experiment.

28 Removing the restriction on the shock size yields a slightly weaker inflation response than in the baseline identification scheme, and inference of inflation worsens notably. Removing the restriction on the historical decomposition does not alter much impulse responses. The historical decomposition is changed as follows. The contribution of financial shocks to the EBP is larger (and more plausible) with the restriction on the historical decomposition. The contribution of financial shocks to inflation dynamics is smaller without the restriction on the shock size. Moreover, the pre-GFC credit and GDP boom is explained less by financial shocks when the restriction on the shock size is removed.

29 See Bernanke (Reference Bernanke2009).

30 The Nakamura and Steinsson (Reference Nakamura and Steinsson2018) measure is available until early 2014.

31 For further details we refer to Nakamura and Steinsson (Reference Nakamura and Steinsson2018).

32 The Federal Reserve acted as a liquidity provider to counter the disruption of normal channels of borrowing and payments after September 11, 2001 (Meyer (Reference Meyer2001)).

33 See Bernanke (Reference Bernanke2009) and Kohn (Reference Kohn2010) for details on the liquidity provision to financial institutions during the GFC by the Federal Reserve.

34 For that purpose we extend the Nakamura and Steinsson (Reference Nakamura and Steinsson2018) shocks, which are available from 1995 onwards, with the monetary policy shocks constructed by Romer and Romer (Reference Romer and Romer2004).

35 See for instance, Khan and Thomas (Reference Khan and Thomas2013) and Petrosky-Nadeau (Reference Petrosky-Nadeau2013) who consider the effects of financial shocks on productivity.

36 Alquist et al. (Reference Alquist, Kilian, Vigfusson, Elliott and Timmermann2013) argue that while the WTI oil price has been regulated before the mid-1980s, it is a reasonable oil price measure since the mid-1980s, which overlaps with our sample period.

37 They use 1-quarter ahead inflation expectations. In line with the year-on-year inflation variable in our model, we use 1-year ahead inflation expectations.

38 The weights are one half of the share of credit market instruments in total business credit, for the commercial paper rate and the Baa spread, respectively, and the share of commercial and industrial loans in total business credit for the C&I loan spread.

References

Abbate, A., Eickmeier, S., Lemke, W. and Marcellino, M. (2016) The changing international transmission of financial shocks: Evidence from a classical time-varying FAVAR. Journal of Money, Credit and Banking 48, 573601.CrossRefGoogle Scholar
Adrian, T., Moench, E. and Shin, H. (2016) Dynamic Leverage Asset Pricing. CEPR Discussion Papers 11466, C.E.P.R. Discussion Papers.Google Scholar
Alquist, R., Kilian, L. and Vigfusson, R. (2013) Forecasting the price of oil. In: Elliott, G. and Timmermann, A. (eds.), Handbook of Economic Forecasting, vol. 2, pp. 427507. Amsterdam: North-Holland.Google Scholar
Ang, A. and Piazzesi, M. (2003) A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables. Journal of Monetary Economics 50(4), 745787.CrossRefGoogle Scholar
AntolÍn-DÍaz, J. and Rubio-RamÍrez, J. F. (2018) Narrative sign restrictions for SVARs. American Economic Review 108(10), 28022829.CrossRefGoogle Scholar
Antoun de Almeida, L. (2015) Firms’ balance sheets and sectoral inflation in the euro area during the financial crisis. Economics Letters 135, 3133.CrossRefGoogle Scholar
Arias, J., Rubio-RamÍrez, J. and Waggoner, D. (2018) Inference based on SVARs identified with sign and zero restrictions: Theory and applications. Technical report.CrossRefGoogle Scholar
Arias, J. E., Caldara, D. and Rubio-RamÍrez, J. F. (2019) The systematic component of monetary policy in SVARs: An agnostic identification procedure. Journal of Monetary Economics 101(C), 113.CrossRefGoogle Scholar
Balleer, A., Hristov, N. and Menno, D. (2015) Financial market imperfections and the pricing decision of firms: Theory and evidence. No 1173, 2015 Meeting Papers from Society for Economic Dynamics.Google Scholar
Barsky, R. and Sims, E. (2011) News shocks and business cycles. Journal of Monetary Economics 58 (3), 273289.CrossRefGoogle Scholar
Barth, M. and Ramey, V. (2001) The cost channel of monetary transmission. In: Bernanke, B. and Rogoff, K. (eds.), NBER Macroeconomics Annual 2001, vol. 16, pp. 199–240. MIT Press.CrossRefGoogle Scholar
Bean, C., Paustian, M., Penalver, A. and Taylor, T. (2010) Monetary policy after the fall. Mimeo. Bank of England; paper presented at the Federal Reserve Bank of Kansas City Annual Conference, Jackson Hole, Wyoming.Google Scholar
Bernanke, B. (2009) The crisis and the policy response. Speech at the Stamp Lecture, London School of Economics, London, England, January 13, 2009.Google Scholar
Bernanke, B. (2010a) The economic outlook and monetary policy. Speech at the Federal Reserve Bank of Kansas City Economic Syposium, Jackson Hole, Wyoming, August 27, 2010.Google Scholar
Bernanke, B., Gertler, M. and Gilchrist, S. (1999) The financial accelerator in a quantitative business cycle framework. In: Taylor, J. B. and Woodford, M. (eds.), Handbook of Macroeconomics, pp. 13411393. Elsevier.CrossRefGoogle Scholar
Bernanke, B. S. (2018) The real effects of disrupted credit: Evidence from the global financial crisis. Brookings Papers on Economic Activity 49(2 (Fall)), 251–342.CrossRefGoogle Scholar
Bjoernland, H. and Leitemo, K. (2009) Identifying the interdependence between US monetary policy and the stock market. Journal of Monetary Economics 56(2), 275282.CrossRefGoogle Scholar
Bobeica, E. and Jarocinski, M. (2017) Missing disinflation and missing inflation: The puzzles that aren’t. ECB Research Bulletin 30, 15.Google Scholar
Bobeica, E. and Jarocinski, M. (2019) Missing disinflation and missing inflation: A VAR perspective. International Journal of Central Banking 15(1), 199232.Google Scholar
Busch, U., Scharnagl, M. and Scheithauer, J. (2010) Loan Supply in Germany During the Financial Crisis. Deutsche Bundesbank Discussion Paper, 2010/05.CrossRefGoogle Scholar
Caldara, D., Fuentes-Albero, C., Gilchrist, S. and ZakrajŠek, E. (2016) The macroeconomic impact of financial and uncertainty shocks. European Economic Review 88(C), 185207.CrossRefGoogle Scholar
Canova, F., Gambetti, L. and Pappa, E. (2007) The structural dynamics of output growth and inflation: Some international evidence. Economic Journal 17(519), C167C191.CrossRefGoogle Scholar
Carlstrom, C. T., Fuerst, T. S., Ortiz, A. and Paustian, M. (2014) Estimating contract indexation in a Financial Accelerator Model. Journal of Economic Dynamics and Control 46(C), 130149.CrossRefGoogle Scholar
Carlstrom, C. T., Fuerst, T. S. and Paustian, M. (2016) Optimal contracts, aggregate risk, and the financial accelerator. American Economic Journal: Macroeconomics 8(1), 119147.Google Scholar
Castelnuovo, E. (2012) Testing the structural interpretation of the price puzzle with a cost-channel model. Oxford Bulletin of Economics and Statistics 74(3), 425452.CrossRefGoogle Scholar
Castelnuovo, E. and Surico, P. (2010) Monetary policy, inflation expectations and the price puzzle. Economic Journal 120(549), 12621283.CrossRefGoogle Scholar
Christiano, L., Motto, R. and Rostagno, M. (2010) Financial Factors in Economic Fluctuations. ECB Working Paper, 1192.CrossRefGoogle Scholar
Christiano, L. J., Eichenbaum, M. S. and Trabandt, M. (2014) Understanding the Great Recession. NBER Working Papers 20040, National Bureau of Economic Research, Inc.CrossRefGoogle Scholar
Coibion, O. and Gorodnichenko, Y. (2015) Is the Phillips Curve alive and well after all? Inflation expectations and the missing disinflation. American Economic Journal: Macroeconomics 7(1), 197232.Google Scholar
Conti, A., Neri, S. and Nobili, A. (2015) Why is Inflation so Low in the Euro Area? Temi di discussione (Economic Working Papers), Bank of Italy, Economic Research and International Relations Area, 1019.CrossRefGoogle Scholar
Covas, J. and Driscoll, J. (2013) The macroeconomic impact of adding liquidity regulations to bank capital regulations. Federal Reserve Bank of San Francisco, mimeo.Google Scholar
Curdia, V. and Woodford, M. (2010) Credit spreads and monetary policy. Journal of Money, Credit and Banking 42(s1), 335.CrossRefGoogle Scholar
De Fiore, F. and Tristani, O. (2013) Optimal monetary policy in a model of the credit channel. Economic Journal 123(571), 906931.CrossRefGoogle Scholar
De NicolÒ, G. and Lucchetta, M. (2011) Systemic risks and the macroeconomy. In: Quantifying Systemic Risk, NBER Chapters, pp. 113–148. National Bureau of Economic Research, Inc.CrossRefGoogle Scholar
De Santis, R. and Darracq-Paries, M. (2015) A non-standard monetary policy shock: The ECB’s 3-year LTROs and the shift in credit supply. Journal of International Money and Finance 54(1), 134.Google Scholar
Del Negro, M., Giannoni, M. and Schorfheide, F. (2015) Inflation in the great recession and New Keynesian models. American Economic Journal: Macroeconomics 7(1), 168196.Google Scholar
Edge, R. and Meisenzahl, R. (2011) The unreliability of credit-to-GDP ratio gaps in real time: Implications for countercyclical capital buffers. International Journal of Central Banking 7(4), 261298.Google Scholar
Eickmeier, S., Metiu, N. and Prieto, E. (2016) Time-Varying Volatility, Financial Intermediation and Monetary Policy. CAMA Working Papers, 2016-32.Google Scholar
Eickmeier, S. and Ng, T. (2015) How do US credit supply shocks propagate internationally? A GVAR approach. European Economic Review 74, 128145.CrossRefGoogle Scholar
Fernald, J. (2012) A Quarterly, Utilization-Adjusted Series on Total Factor Productivity. FRBSF Working Paper, 2012-19.CrossRefGoogle Scholar
FernÁndez-Villaverde, J., GuerrÓn-Quintana, P. A., Kuester, K. and Rubio-RamÍrez, J. (2015) Fiscal volatility shocks and economic activity. American Economic Review 105(11), 3352–84.CrossRefGoogle Scholar
Fornari, F. and Stracca, L. (2012) What does a financial shock do? first international evidence. Economic Policy 27(71), 407455.CrossRefGoogle Scholar
Fry-McKibbin, R. and Zheng, J. (2016) Effects of us monetary policy shocks during financial crises - a threshold vector autoregression approach. Applied Economics 48(59), 58025823.CrossRefGoogle Scholar
Furlanetto, F., Ravazzolo, F. and Sarferaz, S. (2017) Identification of financial factors in economic fluctuations. Economic Journal 129(617), 311337.CrossRefGoogle Scholar
Gaiotti, E. and Secchi, A. (2006a) Is there a cost channel of monetary policy transmission? An investigation into the pricing behavior of 2000 firms. Journal of Money, Credit and Banking 38(8), 20132037.CrossRefGoogle Scholar
Gambetti, L. and Musso, A. (2017) Loan supply shocks and the business cycle. Journal of Applied Econometrics 32(4), 764782.CrossRefGoogle Scholar
Gelain, P., Lansing, K. J. and Natvik, G. J. (2017) Leaning against the credit cycle. Journal of the European Economic Association 16(5), 13501393.CrossRefGoogle Scholar
Gerali, A., Neri, S., Sessa, L. and Signoretti, F. M. (2010) Credit and banking in a DSGE model of the euro area. Journal of Money, Credit and Banking 42(s1), 107141.CrossRefGoogle Scholar
Gertler, M. and Karadi, P. (2011) A model of unconventional monetary policy. Journal of Monetary Economics 58(1), 1734.CrossRefGoogle Scholar
Gertler, M. and Karadi, P. (2015). Monetary policy surprises, credit costs, and economic activity. American Economic Journal: Macroeconomics 7(1), 4476.Google Scholar
Gilchrist, S., Schoenle, R., Sim, J. and ZakrajŠek, E. (2017) Inflation dynamics during the financial crisis. American Economic Review 107(3), 785823.CrossRefGoogle Scholar
Gilchrist, S. and ZakrajŠek, E. (2012) Credit spreads and business cycle fluctuations. Amerian Economic Review 102(4), 16921720.CrossRefGoogle Scholar
Hall, R. (2011) The long slump. American Economic Review 101(2), 431469.CrossRefGoogle Scholar
Helbling, T., Huidrom, R., Kose, M. and Otrok, C. (2011a) Do credit shocks matter? A global perspective. European Economic Review 55(3), 340353.CrossRefGoogle Scholar
Hristov, N. and Huelsewig, O. (2017) Unexpected loan losses and bank capital in an estimated DSGE model of the Euro area. Journal of Macroeconomics 54(B), 161186.CrossRefGoogle Scholar
Hristov, N., Huelsewig, O. and Wollmershaeuser, T. (2012) Loan supply shocks during the financial crisis: Evidence for the euro area. Journal of International Money and Finance 31(3), 569592.CrossRefGoogle Scholar
Hubrich, K. and Tetlow, R. (2015) Financial stress and economic dynamics: the transmission of crises. Journal of Monetary Economics 70, 100115.CrossRefGoogle Scholar
Iacoviello, M. (2015) Financial business cycles. Review of Economic Dynamics 18(1), 140164.CrossRefGoogle Scholar
Jurado, K., Ludvigson, S. and Ng, S. (2015) Measuring uncertainty. American Economic Review 105(3), 11771216.CrossRefGoogle Scholar
Khan, A. and Thomas, J. (2013) Credit shocks and aggregate fluctuations in an economy with production heterogeneity. Journal of Political Economy 121(6), 10551107.CrossRefGoogle Scholar
Kim, C. and Nelson, C. (1999) Has the US economy become more stable? A Bayesian approach based on a Markov Switching model of the business cycle. The Review of Economics and Statistics 81(4), 608616.CrossRefGoogle Scholar
Kohn, D. (2010) The Federal Reserve’s Policy Actions during the Financial Crisis and Lessons for the Future. Speech at the Carleton University, Ottawa, Canada, May 13, 2010.Google Scholar
Krippner, L. (2015) Zero Lower Bound Term Structure Modeling: A Practitioner’s Guide. Palgrave-Macmillan, New York.CrossRefGoogle Scholar
Leduc, S. and Liu, Z. (2016) Uncertainty shocks are aggregate demand shocks. Journal of Monetary Economics 82(C), 2035.CrossRefGoogle Scholar
Lhuissier, S. (2017) Financial intermediaries’ instability and euro area macroeconomic dynamics. European Economic Review 98(C), 4972.CrossRefGoogle Scholar
Linde, J. and Trabandt, M. (2019) Resolving the Missing Deflation Puzzle. CEPR Discussion Paper, 13690.Google Scholar
Ludvigson, S., Ma, S. and Ng, S. (2020) Shock Restricted Structural Vector-Autoregression. NBER Working Paper, 23225.Google Scholar
Ludvigson, S., Ma, S. and Ng, S. (forthcoming) Uncertainty and business cycle: Exogenous impulse or endogenous response. American Economic Journal: Macroeconomics.Google Scholar
Mazumder, S. and Ball, L. M. (2011) Inflation Dynamics and the Great Recession. IMF Working Papers 11/121, International Monetary Fund.CrossRefGoogle Scholar
McConell, M. and Perez-Quiros, G., (2000) Output fluctuations in the United States: What has changed since the early 1980s? American Economic Review 90(5), 14641476.CrossRefGoogle Scholar
Meeks, R. (2012) Do credit market shocks drive output fluctuations? evidence from corporate spreads and defaults. Journal of Economic Dynamics & Control 36(4), 568584.CrossRefGoogle Scholar
Meh, C. A. and Moran, K. (2010) The role of bank capital in the propagation of shocks. Journal of Economic Dynamics and Control 34(3), 555576.CrossRefGoogle Scholar
Meyer, L. (2001) Before and after. Remarks before the National Association of Business Economics, St. Louis, Missouri, November 27, 2001.Google Scholar
Mumtaz, H., Pinter, G. and Theodoridis, K. (2018) What do VARs tell us about the impact of credit supply shock? International Economic Review 59(2), 625646.CrossRefGoogle Scholar
Mumtaz, H. and Theodoridis, K. (2016) The changing transmission of uncertainty shocks in the US: An empirical analysis. Journal of Business & Economic Statistics 36(2), 239252.CrossRefGoogle Scholar
Nakamura, E. and Steinsson, J. (2018) High-frequency identification of monetary non-neutrality: The information effect. The Quarterly Journal of Economics 133(3), 1283–1330.CrossRefGoogle Scholar
Nekarda, C. J. and Ramey, V. A. (2013) The Cyclical Behavior of the Price-Cost Markup. NBER Working Papers 19099, National Bureau of Economic Research, Inc.CrossRefGoogle Scholar
Paustian, M. (2007) Assessing sign restrictions. The B.E. Journal of Macoreconomics 7(1), 133.Google Scholar
Peersman, G. (2005) What caused the early millenium slowdown? Evidence based on vector autoregressions. Journal of Applied Econometrics 20(2), 185207.CrossRefGoogle Scholar
Peersman, G. (2010) Macroeconomic consequences of different types of credit market disturbances and non-conventional monetary policy in the euro area. Mimeo, Ghent University.Google Scholar
Peersman, G. (2011) Bank Lending Shocks and the Euro Area Business Cycle. Working Papers of Faculty of Economics and Business Administration 11/766, Ghent University, Faculty of Economics and Business Administration.Google Scholar
Petrosky-Nadeau, N. (2013) TFP during a credit crunch. Journal of Economic Theory 148(3), 11501178.CrossRefGoogle Scholar
Prieto, E., Eickmeier, S. and Marcellino, M. (2016a) Time variation in macro-financial linkages. Journal of Applied Econometrics 31(7), 12151233.CrossRefGoogle Scholar
Primiceri, G. E. (2005) Time varying structural vector autoregressions and monetary policy. Review of Economic Studies 72(3), 821852.CrossRefGoogle Scholar
Romer, C. D. and Romer, D. H. (2004) A new measure of monetary shocks: Derivation and implications. American Economic Review 94(4), 10551084.CrossRefGoogle Scholar
Van Zandweghe, W. (2019) The Phillips curve and the missing disinflation from the Great Recession. Kansas Fed Economic Review.CrossRefGoogle Scholar
Villa, S. (2016) Financial frictions in the Euro area and the United States: A Bayesian assessment. Macroeconomic Dynamics 20(5), 13131340.CrossRefGoogle Scholar
Walentin, K. (2014) Business cycle implications of mortgage spreads. Journal of Monetary Economics 67(C), 6277.CrossRefGoogle Scholar
Wu, C. and Xia, F. (2016) Measuring the macroeconomic impact of monetary policy at the zero lower bound. Journal of Money, Credit and Banking 48(2–3), 253291.CrossRefGoogle Scholar
Yellen, J. (2013) Panel discussion on “Monetary policy: Many targets, many instruments. where do we stand?”. Technical report.Google Scholar
Yellen, J. (2015) Financial stability a decade after the onset of the crisis. Speech at the “Fostering a Dynamic Global Recovery” Symposium Sponsored by the Federal Reserve Bank of Kansas City, Jackson Hole, Wyoming.Google Scholar
Figure 0

Table 1. Theoretical responses of inflation to contractionary financial shocks implied by different models: An increase (decrease) is denoted with a + (-) sign. ft refers to De Fiore and Tristani (2013); mm refers to Meh and Moran (2010); gnss refers to Gerali et al. (2010); gssz refers to Gilchrist et al. (2017); gk refers to Gertler and Karadi (2011); cw refers to Curdia and Woodford (2010); hh refers to Hristov and Huelsewig (2017); gln refers to Gelain et al. (2017); cmr refers to Christiano et al. (2010)

Figure 1

Table 2. Restrictions to identify financial, aggregate demand and aggregate supply shocks: This table shows the restrictions imposed on the impulse response functions of the endogenous variables in the baseline VAR to identify financial, aggregate demand and aggregate supply shocks. All restrictions are imposed on impact and are implemented as $\geq 0$ ($\uparrow$) or $\leq 0$ ($\downarrow$). See text for details

Figure 2

Figure 1. Impulse responses to a one-standard deviation contractionary financial shock: Plot shows median impulse responses (solid lines) and 68% credible sets (shaded areas). The responses of GDP and the stock price are in percent; those of the other variables are in percentage points.

Figure 3

Table 3. Forecast error variance decomposition: This table shows the median forecast error variance shares explained by financial shocks, for selected variables in the baseline VAR, at a 4-year horizon

Figure 4

Figure 2. Historical decomposition of key variables: Plot shows the contribution of all shocks to explaining the deviation of key variables from their deterministic component (lines) and the contribution of the financial shocks (bars). Contributions to GDP are in percent; those to the other variables are in percentage points.

Figure 5

Figure 3. Transmission channels of financial shocks relevant for inflation: Plot shows median impulse responses (lines) and 68% credible sets (shaded areas). Responses of investment, consumption, employment, and the real wage are in percent; those of the remaining variables are in percentage points. See text for a description of the variables.

Figure 6

Figure 4. Response of credit supply and banking variables to a one-standard deviation contractionary financial shock: Plot shows median impulse responses (lines) and 68% credible sets (shaded areas). Responses of the willingness to lend to consumers, lending standards, the bank return on assets, the nonperforming loans ratio and the bank capital ratio in percentage points. See text for a description of the variables.

Figure 7

Figure 5. Does the GFC make a difference? Left graph: Impulse response functions of inflation to contractionary financial shocks, median baseline estimate (red solid line), and 68% credible sets (shaded areas), and pre-GFC estimate (blue dotted lines). Right graph: Historical decomposition of inflation, baseline model (all shocks: black solid line, financial shocks: red bars), and counterfactual contributions of financial shocks (based on structural shocks estimated over the full sample and reduced-form parameters estimated over the pre-GFC sample: blue dotted line). See text for details.

Figure 8

Figure 6. Impulse responses to a one-standard deviation contractionary financial shock—role of the narrative restrictions: Plot shows median impulse responses and 68% credible sets in the baseline model (solid red lines and shaded areas), and in the model without narrative restrictions (solid and dotted blue lines). The response of GDP is in percent; those of the other variables are in percentage points.

Figure 9

Figure 7. Historical decomposition of key variables—effects of the narrative restrictions: Plot shows contribution of all shocks to explaining the deviation of key variables from their deterministic component in the baseline model (solid black lines), contribution of financial shocks in the baseline (red bars) and in the model without narrative restrictions (blue dotted lines). Contributions to GDP are in percent; those to the other variables are in percentage points.

Figure 10

Figure 8. Financial and monetary policy shocks: Identified financial shocks (solid line) and Nakamura and Steinsson (2018) monetary policy shocks (dashed line). The shocks are standardized so that negative values represent expansionary monetary policy and financial shocks. Both series have standard deviation of 1 over the considered sample. See text for details.

Figure 11

Figure A1. Inflation and Phillips curve predictions: Plot shows actual headline inflation (thick solid line), and predictions from different Phillips curve specifications, based on backward-looking inflation expectations (red dashed line), household inflation expectations (solid black line), household inflation expectations and oil price inflation (green dashed line), household inflation expectations, oil price inflation, and the excess bond premium (black dotted line). See Appendix A for further details.

Figure 12

Table A.1. Phillips curve estimation results: IV-estimation results for different Phillips curve specifications, with backward-looking inflation expectations (back) or household inflation expectations (hh), with or without the excess bond premium and oil price inflation. Slack is measured by the unemployment gap. Robust standard errors are in parentheses. See Appendix A for further details

Figure 13

Figure C2. Impulse responses to a one-standard deviation contractionary financial shock—effects of the narrative restriction on the shock size: Robustness checks Ia: Plot shows median impulse responses and 68% credible sets in the baseline model (solid red lines and shaded areas), and in the model without narrative restriction (solid and dotted blue lines). The response of GDP is in percent; those of the other variables are in percentage points.

Figure 14

Figure C3. Impulse responses to a one-standard deviation contractionary financial shock—effects of the narrative restriction on the historical decomposition: Robustness checks Ib: Plot shows median impulse responses and 68% credible sets in the baseline model (solid red lines and shaded areas), and in the model without narrative restrictions (solid and dotted blue lines). The response of GDP is in percent; those of the other variables are in percentage points.

Figure 15

Figure C4. Historical decomposition of key variables—effects of each narrative restrictions: Robustness checks Ic: Plot shows contribution of all shocks to explaining the deviation of key variables from their deterministic component (solid black lines), contribution of financial shocks in the baseline model (red bars), in the model without shock size restriction (blue dotted lines), and in the model without restriction on historical decomposition (cyan dashed lines). Contributions to GDP are in percent; those to the other variables are in percentage points.

Figure 16

Figure C5. Effect of contractionary financial shocks on inflation: Robustness checks II: Plot shows median impulse responses (red solid line) and 68% credible sets (shaded areas) from the baseline model. Remaining lines show median impulse responses of inflation based on alternative models. Disentangle from housing shock: adding real estate value to credit to the baseline VAR and restricting it to rise on impact after contractionary financial shocks. Disentangle from tech and tech news shock: simultaneous identification of technology shocks, technology news shocks, financial shocks and aggregate demand shocks. Disentangle from oil shock: adding oil prices to the baseline VAR and restricting the oil price not move on impact after baseline shocks.

Figure 17

Figure C6. Effect of contractionary financial shocks on inflation: Robustness checks III: Plot shows median impulse responses (red solid line) and 68% credible sets (shaded areas) from the baseline model. Remaining lines show median impulse responses of inflation based on alternative models. Disentangle from uncertainty shock: adding uncertainty measure to the baseline VAR and restricting the EBP to rise by more than uncertainty after financial shocks. Following Furlanetto et al. (2017), we normalize uncertainty to have the same standard deviation as the EBP. Add Nakamura–Steinsson shock: adding the monetary policy shocks series of Nakamura and Steinsson (2018) to the baseline VAR. Identify a monetary policy shock: simultaneous identification of monetary policy, financial and aggregate demand and supply shocks.

Figure 18

Figure C7. Effect of contractionary financial shocks on inflation: Robustness checks IV: Plot shows median impulse responses (red solid line) and 68% credible sets (shaded areas) from the baseline model. Remaining lines show median impulse responses of inflation based on alternative models. Core ppi inflation: replacing core cpi inflation with core ppi inflation. Add inflation expectations: adding 1-year ahead inflation expectations to the baseline VAR. Federal funds rate: replacing linked federal funds rate SSR variable with the original federal funds rate series. Two-year rate: replacing linked federal funds rate SSR variable with the 2-year T-Bill rate. Ssr Wu-Xia: replacing SSR of Krippner (2015) with SSR of Wu & Xia (2016).

Figure 19

Figure C8. Effect of contractionary financial shocks on inflation: Robustness checks V: Plot shows median impulse responses (red solid line) and 68% credible sets (shaded areas) from the baseline model. Remaining lines show median impulse responses of inflation based on alternative models. Business credit: replacing total credit growth with business credit growth. Total bank credit: replacing total credit growth with total bank credit growth. C&i loans: replacing total credit growth with commercial and industrial bank loan growth. HP-filtered credit: replacing total credit growth with hp-filtered credit. Composite lending spread: replacing EBP with a composite lending spread.

Figure 20

Figure C9. Effect of contractionary financial shocks on inflation: Robustness checks VI: Plot shows median impulse responses (red solid line) and 68% credible sets (shaded areas) from the baseline model. Remaining lines show median impulse responses of inflation based on alternative models. Long sample: re-estimating our model over 1973Q1–2019Q2. Monthly model: re-estimating a monthly model using industrial production instead of GDP.