46 Pages Posted: 20 Sep 2012
Date Written: September 2012
This paper compares alternative models of time-varying macroeconomic volatility on the basis of the accuracy of point and density forecasts of macroeconomic variables. In this analysis, we consider both Bayesian autoregressive and Bayesian vector autoregressive models that incorporate some form of time-varying volatility, precisely stochastic volatility (both with constant and time-varying autoregressive coefficients), stochastic volatility following a stationary AR process, stochastic volatility coupled with fat tails, GARCH, and mixture-of-innovation models. The comparison is based on the accuracy of forecasts of key macroeconomic time series for real-time post–War-II data both for the United States and United Kingdom. The results show that the AR and VAR specifications with widely used stochastic volatility dominate models with alternative volatility specifications, in terms of point forecasting to some degree and density forecasting to a greater degree.
Keywords: Stochastic volatility, GARCH, forecasting
JEL Classification: E17, C11, C53
Suggested Citation: Suggested Citation
Clark, Todd E. and Ravazzolo, Francesco, The Macroeconomic Forecasting Performance of Autoregressive Models with Alternative Specifications of Time-Varying Volatility (September 2012). FRB of Cleveland Working Paper No. 12-18. Available at SSRN: https://ssrn.com/abstract=2149697