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Discretization of Highly-Persistent Correlated AR(1) Shocks

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Abstract

The finite state Markov-Chain approximation method developed by Tauchen (1986) and Tauchen and Hussey (1991) is widely used in economics, finance and econometrics in solving for functional equations where state variables follow an autoregressive process. For highly persistent processes, the method requires a large number of discrete values for the state variables to produce close approximations which leads to an undesirable reduction in computational speed, especially in multidimensional case. This paper proposes an alternative method of discretizing vector autoregressions. The method works well as an approximation and its numerical efficiency applies to a wide range of the parameter space.

Suggested Citation

  • Damba Lkhagvasuren & Ragchaasuren Galindev, 2008. "Discretization of Highly-Persistent Correlated AR(1) Shocks," Working Papers 08012, Concordia University, Department of Economics, revised Nov 2008.
  • Handle: RePEc:crd:wpaper:08012
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    References listed on IDEAS

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    1. Mortensen, Dale & Pissarides, Christopher, 2011. "Job Creation and Job Destruction in the Theory of Unemployment," Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 1-19.
    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. Tauchen, George & Hussey, Robert, 1991. "Quadrature-Based Methods for Obtaining Approximate Solutions to Nonlinear Asset Pricing Models," Econometrica, Econometric Society, vol. 59(2), pages 371-396, March.
    4. Lu Zhang, 2005. "The Value Premium," Journal of Finance, American Finance Association, vol. 60(1), pages 67-103, February.
    5. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
    6. 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.
    7. Flodén, Martin, 2008. "A note on the accuracy of Markov-chain approximations to highly persistent AR(1) processes," Economics Letters, Elsevier, vol. 99(3), pages 516-520, June.
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    Cited by:

    1. Sarolta Laczo & Raffaele Rossi, 2014. "Time-consistent consumption taxation," Working Papers 67495267, Lancaster University Management School, Economics Department.
    2. Paul Gomme & Damba Lkhagvasuren, 2011. "The Cyclicality of Search Intensity in a Competitive Search Model," Working Papers 11003, Concordia University, Department of Economics.
    3. Rabitsch, Katrin & Stepanchuk, Serhiy & Tsyrennikov, Viktor, 2015. "International portfolios: A comparison of solution methods," Journal of International Economics, Elsevier, vol. 97(2), pages 404-422.
    4. repec:fip:fedreq:y:2011:i:3q:p:255-326:n:vol.97no.3 is not listed on IDEAS
    5. 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.
    6. Lkhagvasuren, Damba, 2014. "Education, mobility and the college wage premium," European Economic Review, Elsevier, vol. 67(C), pages 159-173.
    7. Fatih Guvenen, 2011. "Macroeconomics with hetereogeneity : a practical guide," Economic Quarterly, Federal Reserve Bank of Richmond, issue 3Q, pages 255-326.
    8. Sergio J. Rey & Wei Kang & Levi Wolf, 2016. "The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics," Journal of Geographical Systems, Springer, vol. 18(4), pages 377-398, October.
    9. Gomme, Paul & Lkhagvasuren, Damba, 2015. "Worker search effort as an amplification mechanism," Journal of Monetary Economics, Elsevier, vol. 75(C), pages 106-122.
    10. Hernan Seoane, 2016. "Time-varying volatility, default and the sovereign risk premium," 2016 Meeting Papers 1132, Society for Economic Dynamics.
    11. Hansen, Jörgen & Lkhagvasuren, Damba, 2015. "New Evidence on Mobility and Wages of the Young and the Old," IZA Discussion Papers 9258, Institute for the Study of Labor (IZA).
    12. Zhao, Yan, 2013. "Borrowing constraints and the trade balance–output comovement," Economic Modelling, Elsevier, vol. 32(C), pages 34-41.
    13. Pawel Krolikowski, 2017. "Job Ladders and Earnings of Displaced Workers," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(2), pages 1-31, April.
    14. Auray Stéphane & Fuller David & Lkhagvasuren Damba & Terracol Antoine, 2017. "Dynamic Comparative Advantage, Directed Mobility Across Sectors, and Wages," Working Papers 2017-59, Center for Research in Economics and Statistics.
    15. Damba Lkhagvasuren, 2005. "Big Locational Differences in Unemployment Despite High Labor Mobility," Working Papers 12002, Concordia University, Department of Economics, revised Feb 2012.
    16. Italo Lopez Garcia, 2015. "Human Capital and Labor Informality in Chile A Life-Cycle Approach," Working Papers WR-1087, RAND Corporation.
    17. Bommier, Antoine & Harenberg, Daniel & Le Grand, François, 2017. "Household Finance and the Value of Life," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168189, Verein für Socialpolitik / German Economic Association.
    18. Nikolay Gospodinov & Damba Lkhagvasuren, 2014. "A Moment‐Matching Method For Approximating Vector Autoregressive Processes By Finite‐State Markov Chains," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 843-859, August.
    19. Gospodinov, Nikolay & Lkhagvasuren, Damba, 2011. "A new method for approximating vector autoregressive processes by finite-state Markov chains," MPRA Paper 33827, University Library of Munich, Germany.

    More about this item

    Keywords

    Finite State Markov-Chain Approximation; Transition Matrix; Numerical Methods; VAR;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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