Search

This paper examines the evolution of the distribution of industry-specific business cycle linkages, which are modeled through a multivariate Markov-switching model and estimated by Gibbs sampling. Using nonparametric density estimation approaches, we find that the number and location of modes in the distribution of industrial dissimilarities change over the business cycle. There is a relatively stable trimodal pattern during expansionary and recessionary phases characterized by highly, moderately, and lowly synchronized industries. However, during phase changes, the density mass spreads from moderately synchronized industries to lowly synchronized industries. This agrees with a sequential transmission of the industrial business cycle dynamics.

Este articulo propone un modelo econometrico de regimenes markovianos para identificar endogenamente periodos en los que las economias tienden a experimentar fases del ciclo economico de manera sincronizada e independiente. La fiabilidad del modelo econometrico se ha validado con datos simulados utilizando experimentos de Monte-carlo. El modelo se aplica para identificar cambios en la sincronizacion regional de Estados Unidos (EEUU). Los resultados indican la presencia de cambios significativos en los patrones ciclicos de afiliacion de los estados de EEUU, y muestran que, cuanto mas similares son las estructuras economicas de los estados, mayor es la correlacion entre sus ciclos economicos. Adicionalmente, un analisis de redes, basado en las medidas de sincronizacion estimadas, revela un cambio en la propagacion de choques contractivos a traves de estados, lo que sugiere que EEUU se ha sincronizado internamente con mayor intensidad desde principios de los anos noventa.

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where paramet...

This paper analyses changes in economic regional interlinkages in Europe over time and investigates the factors that could explain the dynamics of these changes. Our four main findings are the following: (i) we detect a significant surge in regional synchronisation after the Great Recession; (ii) we identify the regions most interrelated with the rest of Europe, namely, Ile de France, Inner London and Lombardia; (iii) we find that sectoral composition explains regional synchronisation in Europe, mainly after the Great Recession and (iv) we document that sectoral composition has important implications for aggregate economic fluctuations, in particular, that similarities in services-related sectors across regions explain a nonlinear relationship between sectoral composition and regional business cycle synchronisation. We also propose a new method to measure time-varying synchronisation in small samples that combines regime-switching models and dynamic model averaging.

In this paper, we provide a comprehensive analysis of the time-varying interdependence among the economic cycles of the major world economies during the post-Great Moderation period. We document a significant increase in the global business cycle interdependence occurred in the early 2000s. Such increase is mainly attributed to the emerging market economies, since their business cycles became more synchronized with the rest of the world around that time. Moreover, we find that the increase in global interdependence is highly related to decreasing differences in sectoral composition among countries.

This paper provides early assessments of current U.S. Nominal GDP growth, which has been considered as a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy makers have available at the time predictions are made. However, real time information arrives at different frequencies and asynchronously, which poses the challenge of mixed frequencies, missing data, and ragged edges. This paper proposes a multivariate state space model that not only takes into account asynchronous information inflow it also allows for potential parameter instability (DYMIBREAK). We use small scale confirmatory factor analysis in which the candidate variables are selected based on their ability to forecast nominal GDP. The model is fully estimated in one step using a nonlinear Kalman filter, which is applied to obtain simultaneously both optimal inferences on the dynamic factor and parameters. Differently from principal component analysis, the proposed factor model captures the comovement rather than the variance underlying the variables. We compare the predictive ability of the model with other univariate and multivariate specifications. The results indicate that the proposed model containing information on real economic activity, inflation, interest rates, and Divisia monetary aggregates produces the most accurate real time nowcasts of nominal GDP growth.

The recent increase in China’s house prices at the national level masks tremendous variation at the city level – a feature largely overlooked in the macroprudential literature. This paper measures the evolving heterogeneity in China’s house price dynamics across 70 major cities and assesses its relationship with housing market characteristics. We gauge the heterogeneity of house price dynamics using a novel regime-switching modelling approach to estimate the time-varying patterns of China’s city-level housing price synchronization. The estimates indicate an increasing synchronization leading up to 2015, and a decoupling pattern thereafter, which is associated to the heterogeneous strength of regional macroprudential policies. After sorting city-level housing prices into four clusters sharing similar cyclical features, we document high synchronization within clusters, but low synchronization among them. The empirical evidence suggests that differentials in the growth of population, income, and air quality are relevant explanatory factors of housing price synchronization among cities.

Previous studies have shown that the effectiveness of monetary policy depends, to a large extent, on the market expectations of its future actions. This paper proposes an econometric framework to address the effect of the current state of the economy on monetary policy expectations. Specifically, we study the effect of contractionary (or expansionary) demand (or supply) shocks hitting the euro area countries on the expectations of the ECB's monetary policy in two stages. In the first stage, we construct indexes of real activity and inflation dynamics for each country, based on soft and hard indicators. In the second stage, we use those indexes to provide assessments on the type of aggregate shock hitting each country and assess its effect on monetary policy expectations at different horizons. Our results indicate that expectations are responsive to aggregate contractionary shocks, but not to expansionary shocks. Particularly, contractionary demand shocks have a negative effect on short-term monetary policy expectations, while contractionary supply shocks have negative effect on medium- and long-term expectations. Moreover, shocks to different economies do not have significantly different effects on expectations, although some differences across countries arise.

This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In particular, we extend two existing classes of combination schemes – Bayesian (static) model averaging and dynamic model averaging – so as to explicitly reflect the objective of forecasting a discrete outcome. Both simulation and empirical exercises show that our new combination schemes outperform competing combination schemes in terms of forecasting accuracy. In the empirical application, we estimate and forecast U.S. business cycle turning points with state-level employment data. We find that forecasts obtained with our best combination scheme provide timely updates of the U.S. business cycles., Les auteurs présentent de nouvelles méthodes de pondération pour la combinaison de prévisions de variables discrètes issues de différents modèles de Markov à changement de régime. Plus particulièrement, ils étendent deux classes existantes de méthodes de combinaison – combinaison de prévisions établies au moyen de modèles bayésiens (statiques) et combinaison dynamique de prévisions – de manière à correspondre explicitement à l’objectif assigné à l’exercice de prévision d’une variable discrète. Les simulations et l’application empirique montrent qu’en ce qui a trait à l’exactitude des prévisions, les nouvelles méthodes de combinaison surclassent les méthodes de combinaison concurrentes. S’agissant de l’application empirique, les auteurs estiment et prévoient les points de retournement du cycle de l’économie américaine à l’aide de données sur l’emploi provenant des États. Ils constatent que les prévisions obtenues à partir de leur meilleure méthode de combinaison fournissent des renseignements en temps opportun sur les cycles économiques aux États-Unis.