Moving average stochastic volatility models with application to inflation forecast

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Journal Article
Journal of Econometrics, 2013, 176 (2), pp. 162 - 172
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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. © 2013 Elsevier B.V. All rights reserved.
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