This paper studies stock price comovements in two key regions [the United States and Europe, which is represented by three major European developed countries (France, Germany, and the United Kingdom)]. Our paper uses recent high-frequency data (HFD) and investigates price comovements in the context of “normal times” and crisis periods. To this end, we applied a non-Gaussian Asymmetrical Dynamic Conditional Correlation (ADCC)-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model and the Marginal Expected Shortfall (MES) approach. This choice has three advantages: (i) With the development of high-frequency trading (HFT), it is more appropriate to use HFD to test price linkages for overlapping and nonoverlapping data. (ii) The ADCC-GARCH model captures further asymmetry in price comovements. (iii) The use of the MES enables to measure systemic risk contributions around the distribution tails. Accordingly, we offer two interesting findings. First, while the hypothesis of asymmetrical and time-varying stock return linkages is not rejected, the MES approach indicates that both European and US indices make a considerable contribution to each other's systemic risk, with significant input from Frankfurt to the French and US markets, especially following the collapse of Lehman Brothers. Second, we show that the propagation of systemic risk is higher during the crisis period and overlapping trading hours than during nonoverlapping hours. Thus, the MES test is recommended as an indicator to help monitor market exposure to systemic risk and to gauge expected losses for other markets.

In recent decades, the structure of wages in the U.S. economy has shifted to favor workers with college degrees over those without college degrees. Concurrently, there has been an increase in the share of the workforce that is college educated. We build an overlapping generations model in which skill-biased technological change drives both rising wage inequality and a rising percentage of skilled (educated) workers in the labor force. We explore the implications for agent welfare and for the distribution of income of different factor taxation choices. We find that higher tax rates on capital and lower tax rates on unskilled labor can yield steady-state welfare gains across a heterogeneous population, and that these gains increase as the economy experiences technological change that favors skilled labor. Moreover, these shifts in taxation can lower net wage inequality. Steady-state welfare gains, however, come at the expense of agents alive upon implementation.

We formulate a central bank's problem of selecting an optimal long-run inflation rate as the choice of a distorting tax by a planner who wishes to maximize discounted stationary utility for a heterogeneous population of infinitely lived households in an economy with constant aggregate income and public information. Households are segmented into agents who store value in currency alone and agents who have access to both currency and loans. We show that the optimum inflation rate is positive, because inflation reduces the value of the outside option for credit agents and raises their debt limits.

In this paper we investigate the growth effect of environmental taxes when time preference is endogenously determined by environmental quality. We find that if people become more patient because of a cleaner environment, raising the environmental tax may reduce pollution and stimulate growth. Moreover, the Pigouvian principle may be inefficient in the presence of endogenous time preference.

We examine U.S. monetary policies from 1973 to 2014 with the Taylor rule as a benchmark by utilizing a k-state Markov regime-switching model in which the number and the periods of the regimes are endogenously determined. The model relates the federal funds rate to real time output gaps and inflation forecast. It endogenously identifies the periods of Taylor rule regime and discretionary regimes, consistent with the U.S. experience. The Taylor rule regime also coincides with periods of lower variability in inflation and in real GDP growth.

We analyze the optimal debt reduction problem in an uncertainty context. The social planner has a finite horizon and seeks to minimize the social costs associated with debt repayment by taking into account not only the short-run costs of the policy, but also the long-run costs associated with the outstanding level of debt. We characterize the optimal policy and the dynamics of the debt-to-GDP ratio, showing that it will decrease over time if economic policy is effective enough. We characterize how the evolution of the debt-to-GDP ratio depends on the main parameters and we present a simple calibration based on Greek data to illustrate the implications of our analysis in real-world setups.

This paper presents a Ramsey-like dynamic small open economy with endogenous labor migration. In the model, the domestic economy is free to borrow or lend as much as it wants at the given world interest rate, and individuals are supposed to be free to move from a country to another in response to the emergence of a wage differential between countries. Our analysis can be ideally split in two parts. Initially, we propose a baseline model in which only natives are allowed to save and invest in capital assets and traded bonds, whereas immigrants are credit constrained. Next, we provide an extension in which all individuals, including immigrants, have full access to international financial markets. We find that the steady state is always local indeterminate, and that the adjustment dynamics of the competitive equilibrium is dependent upon the initial level of the immigration ratio.

We study the dynamic macroeconomic effects of fiscal shocks under lump-sum tax financing. To this end, we develop an intertemporal macroeconomic model for a small open economy, featuring monopolistic competition in the intermediate goods market, endogenous (intertemporal) labor supply, and finitely lived households. Fiscal shocks are shown to yield endogenously determined (dampened) cycles for a realistic calibration of the model. Impulse response functions of fiscal policy shocks in the finite-horizon model differ substantially from those resulting from an infinitely lived representative agent model. This can be explained by the presence of Ethier-productivity effects, which increase the size of long-run output multipliers to a greater extent in the infinite-horizon model.

Coefficient drivers are observable variables that feed into time-varying coefficients (TVCs) and explain at least part of their movement. To implement the TVC approach, the drivers are split into two subsets, one of which is correlated with the bias-free coefficient that we want to estimate and the other with the misspecification in the model. This split, however, can appear to be arbitrary. We provide a way of splitting the drivers that takes account of any nonlinearity that may be present in the data, with the aim of removing the arbitrary element in driver selection. We also provide an example of the practical use of our method by applying it to modeling the effect of ratings on sovereign-bond spreads.

Does excluding food and energy prices from the Consumer Price Index (CPI) produce a measure that better captures permanent price changes? To examine this question, we decompose CPI inflation and “core” inflation into their permanent and transitory components, using a correlated unobserved-components model. The stationarity of inflation may be time-varying, so we examine the performance of the core measure of inflation separately for periods in which inflation is I(1) and I(0). For a period in which inflation appears to be I(1), we find that core inflation and the permanent component of overall inflation are closely related, but there are some caveats. For a period in which inflation appears to be I(0), we decompose the core and overall price levels and find that the permanent component of the core CPI is much more volatile than the actual core series and that the core excludes volatile permanent shocks to the overall price level.

Rapid growth in large emerging economies has been spread unequally across their regions leading to growing income disparities. This paper examines the distribution of regional output per capita and the evolution of its shape in Brazil, China, India, and Russia from the mid-1990s to the mid-2010s. A comparative analysis of convergence is conducted using a nonparametric methodology. The results reveal different distribution dynamics across the four economies. Chinese regions with below-median output per capita have the highest probability of transitioning toward higher-income levels, while their Brazilian counterparts are trapped at the bottom of the distribution. Although India has the lowest and Russia one of the highest regional income inequalities, they display similar divergence patterns exemplified by high persistence at both ends of the distribution. Our findings indicate that government spending and the rule of law are the major driving forces behind regional convergence, except in Russia where they have the opposite effect. Innovation and property rights also promote convergence in China, but cause regional divergence in India.

Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.

This paper investigates the potential sources of the mixed evidence found in the empirical literature studying asymmetries in the response of output to monetary policy shocks of different magnitudes. Further, it argues that such mixed evidence is a consequence of the exogenous imposition of the threshold that classifies monetary shocks as small or large. To address this issue, I propose an unobserved-components model of output, augmented by a monetary policy variable, which allows the threshold to be endogenously estimated. The results show strong statistical evidence that the effect of monetary policy on output varies disproportionately with the size of the monetary shock once the threshold is estimated. Meanwhile, the estimates of the model are consistent with a key implication of menu-cost models: smaller monetary shocks trigger a larger response on output.

This paper shows that limited asset-market participation (LAMP) generates an extra inflation bias when the fiscal and the monetary authority play strategically. A fully redistributive fiscal policy eliminates the extra inflation bias, but at the cost of reducing Ricardians' welfare. A fiscal authority that redistributes income only partially reduces the inflation bias, but raises government spending. Although a fully conservative monetary policy is necessary to get price stability, it implies a reduction in liquidity-constrained consumers' welfare, in the absence of redistributive fiscal policies. Finally, under a crisis scenario, none of the policy regimes is able to avoid the fall in economic activity when the increase in the fraction of LAMP is coupled with a negative technology shock, whereas optimal policy can avoid recession when it responds to the increase in LAMP proportion alone.

In this paper, we study the effects of government debt on macroeconomic aggregates in a non-Ricardian framework. We develop a microfounded framework that combines time-varying markups, endogenous labor supply, and overlapping generations based on infinitely lived families. The main contribution of this paper is to provide a new transmission mechanism for public debt through the countercyclical markup movements induced by external deep habits. We analyze the effects of a positive shock to public debt. We show that the interest rate rises, entailing higher markups and a fall in employment and consumption. Interestingly, even without capital, a crowding-out effect of government debt is obtained in the long run. In addition, we show that when prices are sticky, public debt has a short-run expansionary effect, which is strengthened by the presence of deep habits.

We construct a microfounded model of money with Gul–Pesendorfer preferences. In each period, agents are tempted to spend all their money by the end of the period, and they suffer from the forgone utility that could have been obtained by adopting the tempting choice. We find that the Friedman rule may not be optimal. A positive nominal interest rate improves welfare because it reduces the real money balances and renders the temptation less attractive. The welfare gained by deviating from the rule is equivalent to 0.67% of consumption.

When some steps of a complex, multi-step task are automated, the demand for human work in the remaining complementary sub-tasks goes up. In contrast, when the task is fully automated, the demand for human work declines. Upon aggregation to the macroeconomic scale, partial automatability of complex tasks creates a bottleneck of development, where further growth is constrained by the scarcity of essential human work. This bottleneck is removed once the tasks become fully automatable. Theoretical analysis using a two-level nested constant elasticity of substitution production function specification demonstrates that the shift from partial to full automation generates a non-convexity: humans and machines switch from complementary to substitutable, and the share of output accruing to human workers switches from an upward to a downward trend. This process has implications for inequality, the risk of technological unemployment, and the likelihood of a secular stagnation.

I examine the optimal inflation target in a dynamic stochastic New Keynesian model featuring an occasionally binding zero lower bound on nominal interest rate (ZLB). To this end, I first calibrate the shock needed to generate the risk of hitting the ZLB that matches the U.S. data, based on a fully nonlinear method. I then resolve the model with different inflation targets and find that the optimal target is 3.4%. In addition, the optimal inflation target is a nonlinear function of the risk of hitting the ZLB and inflation indexation. It is always greater than 2% if the risk is greater than 2.5% or if the inflation indexation is higher than 0.5. Finally, the linear–quadratic approach overestimates the true optimal inflation target. In particular, based on the benchmark calibration, it generates an optimal target of 5.5%, compared with 3.4% found by the fully nonlinear method.

We adopt mechanism design to study the effects of inflation on output, trade, and capital accumulation. Our theory captures multiple channels for individuals to respond to inflation: search intensity, market participation, and substitution between money and a higher return asset. We characterize constrained efficient allocations and show inflation has nonmonotonic effects on the frequency of trades (extensive margin) and the total quantity traded (intensive margin). The model features monetary superneutrality for low inflation rates, nonlinearities in trading frequencies, and substitution of money for capital for higher inflation rates. While these effects are difficult to capture in previous models, we show how they are intimately related by all being features of an optimal trading mechanism.

We study the impact that financial intermediation can have on productivity through the alleviation of credit constraints in occupation choice and/or an improved allocation of risk, using both static and dynamic structural models as well as reduced-form OLS and IV regressions. Our goal in this paper is to bring these two strands of the literature together. Even though, under certain assumptions, IV regressions can accurately recover the true model-generated local average treatment effect, this is quantitatively different, in order of magnitude and even sign, from other policy impact parameters (e.g., ATE and TT). We also show that laying out clearly alternative models can guide the search for instruments. On the other hand, adding more margins of decision, that is, occupation choice and intermediation jointly, or adding more periods with promised utilities as key state variables, as in optimal multiperiod contracts, can cause the misinterpretation of IV as the causal effect of interest.