The Macroeconomic Forecasting Performance of Autoregressive Models with Alternative Specifications of Time-Varying Volatility

46 Pages Posted: 20 Sep 2012  

Todd E. Clark

Federal Reserve Bank of Cleveland

Francesco Ravazzolo

Free University of Bolzano

Multiple version iconThere are 2 versions of this paper

Date Written: September 2012

Abstract

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

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

Todd E. Clark (Contact Author)

Federal Reserve Bank of Cleveland ( email )

P.O. Box 6387
Cleveland, OH 44101
United States
216-579-2015 (Phone)

Francesco Ravazzolo

Free University of Bolzano ( email )

Bolzano
Italy

Paper statistics

Downloads
165
Rank
132,523
Abstract Views
759