Empirical questions such as whether the Phillips curve or the Okun’s law is stable can often be framed as a model comparison—e.g., comparing a vector autoregression (VAR) in which the coefficients in one equation are constant versus one that has time-varying parameters. We develop Bayesian...

It is well known that different specification choices can give starkly different output gap estimates. To account for model uncertainty, we average estimates over a wide variety of popular specifications using stochastic model specification search. In particular, we consider three types of...

We develop importance sampling methods for computing two popular Bayesian model comparison criteria, namely, the marginal likelihood and deviance information criterion (DIC) for TVP-VARs with stochastic volatility. The proposed estimators are based on the integrated likelihood, which are...

We compare a number of widely used trend-cycle decompositions of output in a formal Bayesian model comparison exercise. This is motivated by the often markedly different results from these decompositions — different decompositions have broad implications for the relative importance of real...

This paper reconciles two widely used trend-cycle decompositions of GDP that give markedly different estimates: the correlated unobserved components model yields output gaps that are small in amplitude, whereas the Hodrick-Prescott (HP) filter generates large and persistent cycles. By embedding...

This paper estimates a New Keynesian model with trend inflation and contrasts Taylor rules featuring fixed versus time-varying inflation target while allowing for passive monetary policy. The estimation is conducted over the Great Inflation and the Great Moderation periods. Time-varying...

The deviance information criterion (DIC) has been widely used for Bayesian model comparison. In particular, a popular metric for comparing stochastic volatility models is the DIC based on the conditional likelihood — obtained by conditioning on the latent variables. However, some recent...

The deviance information criterion (DIC) has been widely used for Bayesian model comparison. However, recent studies have cautioned against the use of the DIC for comparing latent variable models. In particular, the DIC calculated using the conditional likelihood (obtained by conditioning on the...

The empirical support for a DSGE type of real business cycle model with two technology shocks is evaluated using a Bayesian model averaging procedure that makes use of a finite mixture of many models within the class of vector autoregressive (VAR) processes. The linear VAR model is extended to...

The modified harmonic mean is widely used for estimating the marginal likelihood. We investigate the empirical performance of two versions of this estimator: one based on the observed-data likelihood and the other on the complete-data likelihood. Through an empirical example using US and UK...