We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are more flexible than alternative “unfolding” models previously introduced in the literature. We then use these models to estimate revealed preferences for legislators in the U.S. House of Representatives and justices on the U.S. Supreme Court. The results from these applications indicate that the new models provide superior complexity-adjusted performance to various alternatives and that the additional flexibility leads to preferences’ estimates that more closely match the perceived ideological positions of legislators and justices.

We investigate the synchronization of the Eurozone’s government bond yields at different maturities. For this purpose, we combine principal component analysis with random matrix theory. We find that synchronization depends on yield maturity. Short-term yields are not synchronized. Medium- and long-term yields, instead, were highly synchronized early after the introduction of the Euro. Synchronization then decreased significantly during the Great Recession and the European Debt Crisis, to partially recover after 2015. We interpret our empirical results using portfolio theory, and we point to divergence trades as a source of the self-sustained yield asynchronous dynamics. Our results envisage synchronization as a requirement for the smooth transmission of conventional monetary policy in the Eurozone.

I develop and study a factor-augmented quasi-vector autoregressive (FAQVAR) model for economic policy analysis in tumultuous times. An observation-driven framework that exploits the information from the score of the model allows a maximum likelihood estimation. This multivariate FAQVAR model, which assumes a Student t error distribution, is robust to atypical observations such as the global financial crisis and the recent pandemic. The model outperforms the FAVAR moving average model because of the assumed heavy tails that capture the COVID-19 atypical data and other turbulent episodes. An empirical application to the U.S. economy assessing its monetary policy reveals that estimates and impulse responses are stable when considering the sample before and during COVID-19.

Nowcasting, that is, forecasting the current economic conditions, is a key ingredient for decision making, but it is complex, even more so for a small open economy, due to the higher volatility of its GDP. In this paper, we review the required steps, taking Luxembourg as an example. We consider both standard and alternative indicators, used as inputs in several nowcasting methods, including various factor and machine learning models. Overall, mixed frequency dynamic factor models and neural networks perform well, both in absolute terms and in relative terms with respect to a benchmark autoregressive model. The gains are larger during problematic times, such as the financial crisis and the recent Covid period.

We provide a new way of deriving a number of dynamic unobserved factors from a set of variables. We show how standard principal components may be expressed in state space form and estimated using the Kalman filter. To illustrate our procedure, we perform two exercises. First, we use it to estimate a measure of the current account imbalances among northern and southern euro area countries that developed during the period leading up to the outbreak of the euro area crisis, before looking at adjustment in the post-crisis period. Second, we show how these dynamic factors can improve forecasting of the euro exchange rate.

We present a methodology to forecast mortality rates and estimate longevity and mortality risks. The methodology uses generalized dynamic factor models fitted to the differences in the log-mortality rates. We compare their prediction performance with that of models previously described in the literature, including the traditional static factor model fitted to log-mortality rates. We also construct risk measures using vine-copula simulations, which take into account the dependence between the idiosyncratic components of the mortality rates. The methodology is applied to forecast mortality rates for a population portfolio for the UK and to estimate longevity and mortality risks.

This study uses the numeraire portfolio to benchmark insurance stock returns as a natural measure for detecting abnormal insurance stock returns from catastrophic events. The assumptions underlying the efficient markets hypothesis using a numeraire denominated returns approach hold for catastrophic insurance events whereas other more traditional methods such as the market model and Fama-French three factor model often fail, typically due to the accumulation of estimation errors. We construct a portfolio of Australian insurance firms and observe the market reaction to major insured catastrophic events. Using the numeraire denominated returns approach we observe no particular trend in the cumulative abnormal returns of insurance securities following a catastrophic event. Using both the traditional market model and the Fama-French three factor model however, we observe significantly positive cumulative abnormal returns following an insured catastrophic event. The errors inherent in the market model and three factor model for event studies are shown to be eliminated using the numeraire denominated returns approach.

This paper compares the different dynamics of the simple-sum monetary aggregates and the Divisia monetary aggregate indices over time, over the business cycle, and across high and low inflation and interest-rate phases. Although traditional comparisons of the series sometimes suggest that simple-sum and Divisia monetary aggregates share similar dynamics, there are important differences around turning points that cannot be evaluated by their average behavior. We use a factor model with a regime-switching model that separates the common movements underlying the monetary aggregate indices from idiosyncratic variations in each series. We find that the major differences between the simple-sum aggregates and Divisia indices occur around the beginnings and ends of recessions and during some high-interest-rate phases. We note the inferences' policy relevance, which is particularly dramatic at the broadest (M3) level of aggregation. Indeed, as Belongia [Journal of Political Economy, 104 (5) (1996), 1065–1083] has observed in this regard, “measurement matters.”