IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v61y2023i2d10.1007_s10614-021-10222-6.html
   My bibliography  Save this article

Finite-State Markov Chains with Flexible Distributions

Author

Listed:
  • Damba Lkhagvasuren

    (Concordia University
    National University of Mongolia)

  • Erdenebat Bataa

    (National University of Mongolia)

Abstract

Constructing Markov chains with desired statistical properties is of critical importance for many applications in economics and finance. This paper proposes a moment-matching method for generating finite-state Markov chains with flexible distributions, including empirically-relevant processes with non-zero skewness and excess kurtosis. In our method, we preserve the most appealing features of the existing methods, including full analytical tractability and minimal computational cost. The new method derives the key moments of the Markov chain as closed-form functions of its parameters. It thus offers a simple plug-in procedure requiring neither numerical integration nor optimization. Using the method amounts to plugging the targeted values of mean, standard deviation, serial correlation, skewness, and excess kurtosis into a simple procedure. The proposed method outperforms the existing methods over a wide range of the parameter space, especially for leptokurtic processes with characteristic roots close to unity. The supplementary materials include ready-to-use computer codes of the plug-in procedures and practical guidelines.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:61:y:2023:i:2:d:10.1007_s10614-021-10222-6
    DOI: 10.1007/s10614-021-10222-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-021-10222-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-021-10222-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fatih Guvenen, 2009. "An Empirical Investigation of Labor Income Processes," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 12(1), pages 58-79, January.
    2. 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.
    3. Stéphane Bonhomme & Jean-Marc Robin, 2010. "Generalized Non-Parametric Deconvolution with an Application to Earnings Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 491-533.
    4. 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.
    5. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
    6. 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.
    7. Fatih Guvenen & Fatih Karahan & Serdar Ozkan & Jae Song, 2021. "What Do Data on Millions of U.S. Workers Reveal About Lifecycle Earnings Dynamics?," Econometrica, Econometric Society, vol. 89(5), pages 2303-2339, September.
    8. Biscarri, William & Zhao, Sihai Dave & Brunner, Robert J., 2018. "A simple and fast method for computing the Poisson binomial distribution function," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 92-100.
    9. Lkhagvasuren, Damba, 2014. "Education, mobility and the college wage premium," European Economic Review, Elsevier, vol. 67(C), pages 159-173.
    10. 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.
    11. 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.
    12. McLaughlin, Kenneth J & Bils, Mark, 2001. "Interindustry Mobility and the Cyclical Upgrading of Labor," Journal of Labor Economics, University of Chicago Press, vol. 19(1), pages 94-135, January.
    13. Yongsung Chang & Sun-Bin Kim, 2007. "Heterogeneity and Aggregation: Implications for Labor-Market Fluctuations," American Economic Review, American Economic Association, vol. 97(5), pages 1939-1956, December.
    14. Richard Rogerson, 2005. "Sectoral Shocks, Specific Human Capital and Displaced Workers," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(1), pages 89-105, January.
    15. Leland E. Farmer & Alexis Akira Toda, 2017. "Discretizing nonlinear, non‐Gaussian Markov processes with exact conditional moments," Quantitative Economics, Econometric Society, vol. 8(2), pages 651-683, July.
    16. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    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. 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.
    2. Roulleau-Pasdeloup, Jordan, 2023. "Analyzing Linear DSGE models: the Method of Undetermined Markov States," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    3. Gordon, Grey, 2021. "Efficient VAR discretization," Economics Letters, Elsevier, vol. 204(C).
    4. 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.
    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. Stéphane Auray, David Fuller & Damba lkhagvasuren & Antoine Terracol, 2014. "A Dynamic Analysis of Sectoral Mobility, Worker Mismatc and the Wage-Tenure Profiles," Working Papers 2014-12, Center for Research in Economics and Statistics.
    7. Hansen, Jörgen & Lkhagvasuren, Damba, 2015. "New Evidence on Mobility and Wages of the Young and the Old," IZA Discussion Papers 9258, Institute of Labor Economics (IZA).
    8. 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.
    9. Robert Kirkby, 2023. "Quantitative Macroeconomics: Lessons Learned from Fourteen Replications," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 875-896, February.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Leland E. Farmer, 2021. "The discretization filter: A simple way to estimate nonlinear state space models," Quantitative Economics, Econometric Society, vol. 12(1), pages 41-76, January.
    16. Lkhagvasuren, Damba, 2014. "Education, mobility and the college wage premium," European Economic Review, Elsevier, vol. 67(C), pages 159-173.
    17. Tanaka, Ken'ichiro & Toda, Alexis Akira, 2015. "Discretizing Distributions with Exact Moments: Error Estimate and Convergence Analysis," University of California at San Diego, Economics Working Paper Series qt7g23r5kh, Department of Economics, UC San Diego.
    18. Susana MEJIA & Andres Ramirez HASSAN, 2016. "Determining the optimal selling time of cattle: A stochastic dynamic programming approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 62(11), pages 517-527.
    19. Fuller, David L. & Kudlyak, Marianna & Lkhagvasuren, Damba, 2014. "Productivity insurance: The role of unemployment benefits in a multi-sector model," Journal of Economic Dynamics and Control, Elsevier, vol. 47(C), pages 39-53.
    20. Mariacristina De Nardi & Giulio Fella & Gonzalo Paz Pardo, 2016. "The Implications of Richer Earnings Dynamics for Consumption and Wealth," NBER Working Papers 21917, National Bureau of Economic Research, Inc.

    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:kap:compec:v:61:y:2023:i:2:d:10.1007_s10614-021-10222-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.