Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market

Abstract

In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a standard tool. Unfortunately, the approaches currently used for modeling heteroskedasticity all have draw- backs. For instance, assuming known dates for variance changes is often unrealistic while more exible models based on GARCH or Markov switch- ing residuals are dicult to handle from a statistical and computational point of view. Therefore we propose a model based on a smooth change in variance that is exible as well as relatively easy to estimate. The model is applied to a ve-dimensional system of U.S. variables to explore the interaction be- tween monetary policy and the stock market. It is found that previously used conventional identication schemes in this context are rejected by the data