Moving Average Stochastic Volatility Models with Application to Inflation Forecast

Joshua Chan

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

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 U.S. 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)
Pages: 27 pages
Date: 2013-05
New Economics Papers: this item is included in nep-cba, nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (99)

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https://cama.crawford.anu.edu.au/publication/2060/ ... n-inflation-forecast (application/pdf)

Related works:
Journal Article: 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:een:camaaa:2013-31

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