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Moving Average Stochastic Volatility Models with Application to Inflation Forecast

  • Joshua C.C. Chan

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 insample fitness and out-of-sample forecast performance than the standard variants with only stochastic volatility.

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File URL: https://cama.crawford.anu.edu.au/pdf/working-papers/2013/312013.pdf
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Paper provided by Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University in its series CAMA Working Papers with number 2013-31.

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Length: 27 pages
Date of creation: May 2013
Date of revision:
Handle: RePEc:een:camaaa:2013-31
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