Working Paper

The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility

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 coeffi cients), 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: Simulation modeling; Economic forecasting; Bayesian statistical decision theory;

https://doi.org/10.26509/frbc-wp-201218

Bibliographic Information

Provider: Federal Reserve Bank of Cleveland

Part of Series: Working Papers (Old Series)

Publication Date: 2012

Number: 1218