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

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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 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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Keywords: Markov Chain; Vector Autoregressive Processes; Functional Equation; Numerical Methods; Moment Matching;

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References

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  1. 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.
  2. Lkhagvasuren, Damba, 2012. "Big locational unemployment differences despite high labor mobility," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 798-814.
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  6. Damba Lkhagvasuren & Ragchaasuren Galindev, 2008. "Discretization of Highly-Persistent Correlated AR(1) Shocks," Working Papers 08012, Concordia University, Department of Economics, revised Nov 2008.
  7. 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.
  8. Vasco Cúrdia & Ricardo Reis, 2010. "Correlated Disturbances and U.S. Business Cycles," NBER Working Papers 15774, National Bureau of Economic Research, Inc.
  9. 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.
  10. 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.
  11. 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.).
  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. 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.
  15. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
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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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