We propose a new VAR identification scheme that distinguishes shifts of and movements along the labor demand schedule to identify labor-supply shocks. According to our VAR analysis of post-war U.S. data, labor-supply shifts account for about 30 percent of the variation in hours and about 15...

In a recent paper, Chang et al. [2002. Learning-by-doing as a propagation mechanism. American Economic Review 92 (5) 1498-1520] extend the standard real business cycle (RBC) model to allow for a learning-by-doing (LBD) mechanism whereby current labor supply affects future productivity. They show...

We investigate the role of labor-supply shifts in economic fluctuations. A new VAR identification scheme for labor supply shocks is proposed. Our method provides an alternative identification scheme, which does not rely on quot;zero-restrictionsquot;. According to our VAR analysis of post-war...

This paper suggests that skill accumulation through past work experience, or "learning-by-doing", can provide an important propagation mechanism for initial shocks, as the current labor supply affects future productivity. Our econometric analysis uses a Bayesian approach to combine micro-level...

In a recent paper, Chang, Gomes, and Schorfheide (2002) extend the standard real business cycle (RBC) model to allow for a learning-by-doing (LBD) mechanism whereby current labour supply affects future productivity. They show that this feature magnifies the propagation of shocks and improves the...

In a Bayesian analysis, different models can be compared on the basis of theexpected or marginal likelihood they attain. Many methods have been devised to compute themarginal likelihood, but simplicity is not the strongest point of most methods. At the sametime, the precision of methods is often...

This paper proposes a methodology for default probability estimation for low default portfolios, where the statistical inference may become troublesome. The author suggests using logistic regression models with the Bayesian estimation of parameters. The piecewise logistic regression model and...

The Reversible Jump Markov Chain Monte Carlo (RJMCMC) method can enhance Bayesian DSGE estimation by sampling from a posterior distribution spanning potentially nonnested models with parameter spaces of different dimensionality. We use the method to jointly sample from an ARMA process of unknown...

This paper develops a Markov-Switching vector autoregressive model that allows for imperfect synchronization of cyclical regimes in multiple variables, due to phase shifts of a single common cycle. The model has three key features: (i) the amount of phase shift can be different across regimes...