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A Moment-Matching Method for Approximating Vector Autoregressive Processes by Finite-State Markov Chains

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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Paper provided by Concordia University, Department of Economics in its series Working Papers with number 11005.

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Length: 33 pages
Date of creation: 08 Jun 2011
Date of revision: 16 Dec 2011
Handle: RePEc:crd:wpaper:11005
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  1. 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.
  2. Karen Kopecky & Richard Suen, 2010. "Finite State Markov-chain Approximations to Highly Persistent Processes," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 13(3), pages 701-714, July.
  3. Christian Bayer & Falko Juessen, 2012. "On the Dynamics of Interstate Migration: Migration Costs and Self-Selection," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(3), pages 377-401, July.
  4. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
  5. 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.
  6. Cúrdia, Vasco & Reis, Ricardo, 2010. "Correlated Disturbances and U.S. Business Cycles," CEPR Discussion Papers 7712, C.E.P.R. Discussion Papers.
  7. Lkhagvasuren, Damba & Galindev, Ragchaasuren, 2008. "Discretization of highly persistent correlated AR(1) shocks," MPRA Paper 22523, University Library of Munich, Germany.
  8. Nikolay Gospodinov & Alex Maynard & Elena Pesavento, 2011. "Sensitivity of Impulse Responses to Small Low-Frequency Comovements: Reconciling the Evidence on the Effects of Technology Shocks," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(4), pages 455-467, October.
  9. 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.
  10. Lkhagvasuren, Damba, 2012. "Big locational unemployment differences despite high labor mobility," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 798-814.
  11. Gomme, Paul & Rupert, Peter, 2007. "Theory, measurement and calibration of macroeconomic models," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 460-497, March.
  12. 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.
  13. Edward S. Knotek II & Stephen Terry, 2008. "Markov-chain approximations of vector autoregressions: application of general multivariate-normal integration techniques," Research Working Paper RWP 08-02, Federal Reserve Bank of Kansas City.
  14. Nigar Hashimzade & Michael A. Thornton (ed.), 2013. "Handbook of Research Methods and Applications in Empirical Macroeconomics," Books, Edward Elgar, number 14327, July.
  15. Dario Caldara & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Yao Wen, 2012. "Computing DSGE models with recursive preferences and stochastic volatility," Finance and Economics Discussion Series 2012-04, Board of Governors of the Federal Reserve System (U.S.).
  16. 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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