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Moving Average Stochastic Volatility Models with Application to Inflation Forecast

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  • Joshua C C Chan

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Abstract

Moving average and stochastic volatility are two important components for modeling and forecasting macroeconomic and financial time series. The former aims to capture short-run dynamics, whereas the latter allows for volatility clustering and time-varying volatility. We introduce a new class of models that includes both of these useful features. The new models allow the conditional mean process to have a state space form. As such, this general framework includes a wide variety of popular specifications, including the unobserved components and time-varying parameter models. Having a moving average process, 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 this new class of 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.

Suggested Citation

  • Joshua C C Chan, 2012. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," ANU Working Papers in Economics and Econometrics 2012-591, Australian National University, College of Business and Economics, School of Economics.
  • Handle: RePEc:acb:cbeeco:2012-591
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    File URL: https://www.cbe.anu.edu.au/researchpapers/econ/wp591.pdf
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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