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

  • Gospodinov, Nikolay

    ()

    (Federal Reserve Bank of Atlanta)

  • Lkhagvasuren, Damba

    ()

    (Concordia University)

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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File URL: http://www.frbatlanta.org/documents/pubs/wp/wp1305.pdf
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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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  1. Karen A. Kopecky & Richard M. H. Suen, 2009. "Finite State Markov-Chain Approximations to Highly Persistent Processes," Working Papers 200904, University of California at Riverside, Department of Economics, revised May 2009.
  2. Vasco Cúrdia & Ricardo Reis, 2010. "Correlated disturbances and U.S. business cycles," Staff Reports 434, Federal Reserve Bank of New York.
  3. Galindev, Ragchaasuren & Lkhagvasuren, Damba, 2010. "Discretization of highly persistent correlated AR(1) shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 34(7), pages 1260-1276, July.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. Bayer, Christian & Juessen, Falko, 2006. "On the Dynamics of Interstate Migration: Migration Costs and Self-Selection," Technical Reports 2006,38, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  9. 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.
  10. Paul Gomme & Peter Rupert, 2005. "Theory, measurement, and calibration of macroeconomic models," Working Paper 0505, Federal Reserve Bank of Cleveland.
  11. Jerome Adda & Russell W. Cooper, 2003. "Dynamic Economics: Quantitative Methods and Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012014, June.
  12. Lkhagvasuren, Damba, 2012. "Big locational unemployment differences despite high labor mobility," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 798-814.
  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. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
  15. Tauchen, George, 1986. "Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 397-416, October.
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