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

Todd Clark and Francesco Ravazzolo

No 2012/09, Working Paper from Norges Bank

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 (search for similar items in EconPapers)
JEL-codes: C11 C53 E17 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2012-10-09
New Economics Papers: this item is included in nep-ets, nep-for, nep-mac and nep-ore
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Citations: View citations in EconPapers (17)

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Working Paper: The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility (2012)
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