This paper investigates the nature of U.S. business cycle asymmetry using a dynamic factor model of output, investment, and consumption. We identify a common stochastic trend and common transitory component by embedding the permanent income hypothesis within a simple growth model....

This paper presents a new nonlinear time series model that captures a post-recession “bounce-back” in the level of aggregate output. While a number of studies have examined this type of business cycle asymmetry using recession-based dummy variables and threshold models, we relate the...

This paper investigates the dynamic relationship between permanent and transitory components of post-war U.S. business cycles. We specify a time-series model for real GNP and consumption in which the two share a common stochastic trend and transitory component, and Markov-regime switching is...

Using a Bayesian model comparison strategy, we search for a volatility reduction within the post-war sample for the growth rates of U.S. aggregate and disaggregate real GDP. We find that the growth rate of aggregate real GDP has been less volatile since the early 1980s, and that this volatility...

This paper investigates the relationship between permanent and transitory components of U.S. recessions in an empirical model allowing for business cycle asymmetry. Using a common stochastic trend representation for real GNP and consumption, we divide real GNP into permanent and transitory...

This paper investigates the nature of business cycle asymmetry using a dynamic factor model of output, investment, and consumption. We first identify a common stochastic trend and a common transitory component by embedding the permanent income hypothesis within a simple growth model. We then...

Using Bayesian tests for a structural break at an unknown break date, we search for a volatility reduction within the post-war sample for the growth rates of U.S. aggregate and disaggregate real GDP. We find that the growth rate of aggregate real GDP has been less volatile since the early...