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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.

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Paper provided by Australian National University, College of Business and Economics, School of Economics in its series ANU Working Papers in Economics and Econometrics with number 2012-591.

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Length: 26 Pages
Date of creation: Oct 2012
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Handle: RePEc:acb:cbeeco:2012-591

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Citations

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Cited by:
  1. Eric Eisenstat & Rodney W. Strachan, 2014. "Modelling Inflation Volatility," CAMA Working Papers 2014-21, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  2. Nonejad, Nima, 2014. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," MPRA Paper 55662, University Library of Munich, Germany.
  3. Nonejad, Nima, 2014. "Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks," MPRA Paper 55664, University Library of Munich, Germany.
  4. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  5. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  6. Nima Nonejad, 2013. "Long Memory and Structural Breaks in Realized Volatility: An Irreversible Markov Switching Approach," CREATES Research Papers 2013-26, School of Economics and Management, University of Aarhus.
  7. Joshua C.C. Chan & Eric Eisenstat, 2013. "Gibbs Samplers for VARMA and Its Extensions," ANU Working Papers in Economics and Econometrics, Australian National University, College of Business and Economics, School of Economics 2013-604, Australian National University, College of Business and Economics, School of Economics.

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