Time Varying Structural Vector Autoregressions and Monetary Policy

Abstract

Monetary policy and the private sector behavior of the US economy are modeled as a time varying structural vector autoregression, where the sources of time variation are both the coefficients and the variance covariance matrix of the innovations. The paper develops a new, simple modeling strategy for the law of motion of the variance covariance matrix and proposes an efficient Markov chain Monte Carlo algorithm for the model likelihood/posterior numerical evaluation. The main empirical conclusions are: 1) both systematic and non-systematic monetary policy have changed during the last forty years. In particular, long run systematic responses of the interest rate to inflation and unemployment exhibit a trend toward a more aggressive behavior, despite remarkable oscillations; 2) this has had a negligible effect on the rest of the economy. The role played by exogenous non-policy shocks seems much more important than monetary policy in explaining the high inflation and unemployment episodes in recent US economic history.

Keywords: Time varying coefficients, multivariate stochastic volatility, Gibbs sampling, Taylor rule, monetary policy shocks, identification

JEL Classification: C15, C22, E31, E32, E58