Using a panel of U.S. city-level building permits data, we estimate a Markov-switching model of housing cycles that allows for idiosyncratic departures from a national housing cycle. These departures occur for clusters of cities that experience simultaneous housing contractions. We find that...

Using a panel of U.S. city-level building permits data, we estimate a Markov-switching model of housing cycles that allows for idiosyncratic departures from a national housing cycle. These departures occur for clusters of cities that experience simultaneous housing contractions. We find that...

The aim of this paper is to show the usefulness of Finite Mixture Markov Models (FMMMs) for regional analysis. FMMMs combine clustering techniques and Markov Switching models, providing a powerful methodological framework to jointly obtain business cycle datings and clusters of regions that...

We study the impact of graduating in a recession in Flanders (Belgium), i.e. in a rigid labor market. In the presence of a high minimum wage, a typical recession hardly influences the hourly wage of low educated men, but reduces working time and earnings by about 4.5% up to twelve years after...

We study the impact of graduating in a recession in Flanders (Belgium), i.e. in a rigid labor market. In the presence of a high minimum wage, a typical recession hardly influences the hourly wage of low educated men, but reduces working time and earnings by about 4.5% up to twelve years after...

This paper develops a statistical model for measuring spatial interactions when estimating macroeconomic regimes and regime shifts. The model is applied to study the contagion and propagation of recessions in small regional economies in the United States from 1990 to 2015. The empirical analysis...

Business cycles are a central element of the economy. Over the last decades, more and more sophisticated methods and data have become available for business cycle classification. This paper contributes to the field of business cycle and recession estimation by applying a fuzzy c-means clustering...