IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/22523.html
   My bibliography  Save this paper

Discretization of highly persistent correlated AR(1) shocks

Author

Listed:
  • Lkhagvasuren, Damba
  • Galindev, Ragchaasuren

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 a multidimensional case. This paper proposes an alternative method of discretizing vector autoregressions. This method can be treated as an extension of Rouwenhorst's (1995) method which, according to our experiments, outperforms the existing methods in the scalar case for highly persistent processes. The new method works well as an approximation that is much more robust to the number of discrete values for a wide range of the parameter space.

Suggested Citation

  • Lkhagvasuren, Damba & Galindev, Ragchaasuren, 2008. "Discretization of highly persistent correlated AR(1) shocks," MPRA Paper 22523, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:22523
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/22523/1/MPRA_paper_22523.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
    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. 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.
    4. Mortensen, Dale & Pissarides, Christopher, 2011. "Job Creation and Job Destruction in the Theory of Unemployment," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 1-19.
    5. 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.
    6. 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.
    7. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    8. Lu Zhang, 2005. "The Value Premium," Journal of Finance, American Finance Association, vol. 60(1), pages 67-103, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gordon, Grey, 2021. "Efficient VAR discretization," Economics Letters, Elsevier, vol. 204(C).
    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. 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.
    4. Giulio Fella & Giovanni Gallipoli & Jutong Pan, 2019. "Markov-Chain Approximations for Life-Cycle Models," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 34, pages 183-201, October.
    5. 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.
    6. Takefumi Yamazaki, 2018. "Accuracy and speed of the solution methods for sovereign default models: The stable performance of the Tauchen method and cubic spline interpolation," Public Policy Review, Policy Research Institute, Ministry of Finance Japan, vol. 14(4), pages 641-662, July.
    7. Almut Balleer & Georg Duernecker & Susanne K. Forstner & Johannes Goensch, 2021. "The Effects of Biased Labor Market Expectations on Consumption, Wealth Inequality, and Welfare," CESifo Working Paper Series 9326, CESifo.
    8. Gian Luca Clementi & Berardino Palazzo, 2019. "Investment and the Cross‐Section of Equity Returns," Journal of Finance, American Finance Association, vol. 74(1), pages 281-321, February.
    9. Pawel Krolikowski, 2017. "Job Ladders and Earnings of Displaced Workers," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(2), pages 1-31, April.
    10. Jonen, Benjamin & Scheuring, Simon, 2014. "Time-varying international diversification and the forward premium," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 128-148.
    11. Anagnostopoulos Alexis & Tang Xin, 2015. "Evaluating linear approximations in a two-country model with occasionally binding borrowing constraints," The B.E. Journal of Macroeconomics, De Gruyter, vol. 15(1), pages 43-91, January.
    12. Jess Benhabib & Alberto Bisin & Shenghao Zhu, 2011. "The Distribution of Wealth and Fiscal Policy in Economies With Finitely Lived Agents," Econometrica, Econometric Society, vol. 79(1), pages 123-157, January.
    13. Gabrovski, Miroslav & Ortego-Marti, Victor, 2021. "Search and credit frictions in the housing market," European Economic Review, Elsevier, vol. 134(C).
    14. Damba Lkhagvasuren & Erdenebat Bataa, 2023. "Finite-State Markov Chains with Flexible Distributions," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 611-644, February.
    15. Roulleau-Pasdeloup, Jordan, 2023. "Analyzing Linear DSGE models: the Method of Undetermined Markov States," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    16. Wai-Yip Alex Ho & Chun-Yu Ho, 2016. "Inflation, Financial Developments, and Wealth Distribution," IMF Working Papers 2016/132, International Monetary Fund.
    17. Miroslav Gabrovski & Victor Ortego-Marti, 2018. "Housing Market Dynamics with Search Frictions," Working Papers 201804, University of California at Riverside, Department of Economics.
    18. Alexis Akira Toda, 2021. "Data-Based Automatic Discretization of Nonparametric Distributions," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1217-1235, April.
    19. Orazio Attanasio & Renata Bottazzi & Hamish Low & Lars Nesheim & Matthew Wakefield, 2012. "Modelling the Demand for Housing over the Lifecycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(1), pages 1-18, January.
    20. 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.

    More about this item

    Keywords

    Finite State Markov-Chain Approximation; Discretization of Multivariate Autoregressive Processes; Transition Matrix; Numerical Methods; Value Function Iteration; the Rouwenhorst method; VAR;
    All these keywords.

    JEL classification:

    • J60 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:22523. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.