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Moving Average Stochastic Volatility Models with Application to Inflation Forecast

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  • Joshua C.C. Chan

Abstract

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: http://cama.crawford.anu.edu.au/pdf/working-papers/2013/312013.pdf
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Bibliographic Info

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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Keywords: state space; unobserved components model; precision; sparse; density forecast.;

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References

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  1. Chan, Joshua & Koop, Gary & Potter, Simon, 2012. "A new model of trend inflation," MPRA Paper 39496, University Library of Munich, Germany.
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Citations

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Cited by:
  1. Joshua C.C. Chan & Eric Eisenstat, 2013. "Gibbs Samplers for VARMA and Its Extensions," ANU Working Papers in Economics and Econometrics 2013-604, Australian National University, College of Business and Economics, School of Economics.
  2. 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.
  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. 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.
  5. 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.
  6. 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.
  7. 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.

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