The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to Inflation Modeling
CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University
This paper generalizes the popular stochastic volatility in mean model of Koopman and Hol Uspensky (2002) to allow for time-varying parameters in the conditional mean. The estimation of this extension is nontrival since the volatility appears in both the conditional mean and the conditional variance, and its coefficient in the former is time-varying. We develop an efficient Markov chain Monte Carlo algorithm based on band and sparse matrix algorithms instead of the Kalman filter to estimate this more general variant. We illustrate the methodology with an application that involves US, UK and Germany inflation. The estimation results show substantial time-variation in the coefficient associated with the volatility, high-lighting the empirical relevance of the proposed extension. Moreover, in a pseudo out-of-sample forecasting exercise, the proposed variant also forecasts better than various standard benchmarks.
Keywords: nonlinear; state space; inflation forecasting; inflation uncertainty (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 C58 E31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac, nep-mon and nep-ore
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Journal Article: The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2015-07
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