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A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains

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  • Gospodinov, Nikolay

    ()
    (Federal Reserve Bank of Atlanta)

  • Lkhagvasuren, Damba

    ()
    (Concordia University)

Abstract

This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and tends to outperform the existing methods for approximating multivariate processes over a wide range of the parameter space, especially for highly persistent vector autoregressions with roots near the unit circle.

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Bibliographic Info

Paper provided by Federal Reserve Bank of Atlanta in its series Working Paper with number 2013-05.

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Length: 31 pages
Date of creation: 01 Sep 2013
Date of revision:
Handle: RePEc:fip:fedawp:2013-05

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Keywords: Markov chain; vector autoregressive processes; numerical methods; moment matching; non-linear stochastic dynamic models state space discretization; stochastic growth model; fiscal policy;

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  1. Coleman, Wilbur John, II, 1990. "Solving the Stochastic Growth Model by Policy-Function Iteration," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 27-29, January.
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  4. Terry, Stephen J. & Knotek II, Edward S., 2011. "Markov-chain approximations of vector autoregressions: Application of general multivariate-normal integration techniques," Economics Letters, Elsevier, vol. 110(1), pages 4-6, January.
  5. Dario Caldara & Jesus Fernandez-Villaverde & Juan Rubio-Ramirez & Wen Yao, 2012. "Computing DSGE Models with Recursive Preferences and Stochastic Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 188-206, April.
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  7. Kopecky, Karen A. & Suen, Richard M. H., 2009. "Finite State Markov-Chain Approximations to Highly Persistent Processes," MPRA Paper 15122, University Library of Munich, Germany.
  8. Gomme, Paul & Rupert, Peter, 2007. "Theory, measurement and calibration of macroeconomic models," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 460-497, March.
  9. Lkhagvasuren, Damba & Galindev, Ragchaasuren, 2008. "Discretization of highly persistent correlated AR(1) shocks," MPRA Paper 22523, University Library of Munich, Germany.
  10. Lkhagvasuren, Damba, 2012. "Big locational unemployment differences despite high labor mobility," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 798-814.
  11. Tauchen, George & Hussey, Robert, 1991. "Quadrature-Based Methods for Obtaining Approximate Solutions to Nonlinear Asset Pricing Models," Econometrica, Econometric Society, vol. 59(2), pages 371-96, March.
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  13. GOSPODINOV, Nikolay & MAYNARD, Alex & PESAVENTO, Elena, 2009. "Sensitivity of Impulse Responses to Small Low Frequency Co-Movements : Reconciling the Evidence on the Effects of Technology Shocks," Cahiers de recherche 03-2009, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  14. Jerome Adda & Russell W. Cooper, 2003. "Dynamic Economics: Quantitative Methods and Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012014, December.
  15. Tauchen, George, 1986. "Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 423-25, October.
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Cited by:
  1. Viktor Tsyrennikov & Serhiy Stepanchuk & Katrin Rabitsch, 2013. "International Portfolios: A Comparison of Solution Methods," 2013 Meeting Papers 1146, Society for Economic Dynamics.
  2. Katrin Rabitsch & Serhiy Stepanchuk & Viktor Tsyrennikov, 2014. "International Portfolios: A Comparison of Solution Methods," Department of Economics Working Papers wuwp159, Vienna University of Economics, Department of Economics.

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