Moving average stochastic volatility models with application to inflation forecast

Joshua Chan

Journal of Econometrics, 2013, vol. 176, issue 2, 162-172

Abstract: We introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional mean has a state space representation. Having a moving average component, however, means that the errors in the measurement equation are no longer serially independent, and estimation becomes more difficult. We develop a posterior simulator that builds upon recent advances in precision-based algorithms for estimating these new models. In an empirical application involving US inflation we find that these moving average stochastic volatility models provide better in-sample fitness and out-of-sample forecast performance than the standard variants with only stochastic volatility.

Keywords: State space; Unobserved components model; Precision; Sparse; Density forecast (search for similar items in EconPapers)
JEL-codes: C11 C51 C53 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (111)

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Working Paper: Moving Average Stochastic Volatility Models with Application to Inflation Forecast (2013)
Working Paper: Moving Average Stochastic Volatility Models with Application to Inflation Forecast (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:176:y:2013:i:2:p:162-172

DOI: 10.1016/j.jeconom.2013.05.003

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