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A Moment‐Matching Method For Approximating Vector Autoregressive Processes By Finite‐State Markov Chains

Author

Listed:
  • Nikolay Gospodinov
  • Damba Lkhagvasuren

Abstract

SUMMARY 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. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:japmet:v:29:y:2014:i:5:p:843-859
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    Cited by:

    1. Ethan Struby & Michael F. Connolly, 2022. "Shadow Rate Models and Monetary Policy," Working Papers 2022-03, Carleton College, Department of Economics.
    2. Benigno, Gianluca & Chen, Huigang & Otrok, Christopher & Rebucci, Alessandro & Young, Eric R., 2016. "Optimal capital controls and real exchange rate policies: A pecuniary externality perspective," Journal of Monetary Economics, Elsevier, vol. 84(C), pages 147-165.
    3. Laszlo Tetenyi & Karol Mazur, 2024. "The Macroeconomic Impact of Agricultural Input Subsidies," Working Papers w202422, Banco de Portugal, Economics and Research Department.
    4. Eva F. Janssens & Sean McCrary, 2023. "Finite-State Markov-Chain Approximations: A Hidden Markov Approach," Finance and Economics Discussion Series 2023-040, Board of Governors of the Federal Reserve System (U.S.).
    5. 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.
    6. 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 qt2tc0m67t, Department of Economics, UC San Diego.
    7. Lauren E. Cipriano & Thomas A. Weber, 2018. "Population-level intervention and information collection in dynamic healthcare policy," Health Care Management Science, Springer, vol. 21(4), pages 604-631, December.
    8. Xing Guo & Pablo Ottonello & Diego J. Perez, 2023. "Monetary Policy and Redistribution in Open Economies," Journal of Political Economy Macroeconomics, University of Chicago Press, vol. 1(1), pages 191-241.
    9. Keyvan Eslami & Thomas Phelan, 2025. "The Art of Temporal Approximation: An Investigation into Numerical Solutions to Discrete- and Continuous-Time Problems in Economics," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1505-1547, March.
    10. Gordon, Grey, 2021. "Efficient VAR discretization," Economics Letters, Elsevier, vol. 204(C).
    11. Eric Ghysels & Jack Morgan, 2024. "On Quantum Ambiguity and Potential Exponential Computational Speed-Ups to Solving Dynamic Asset Pricing Models," Papers 2405.01479, arXiv.org, revised Aug 2025.
    12. 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.
    13. Roulleau-Pasdeloup, Jordan, 2023. "Analyzing Linear DSGE models: the Method of Undetermined Markov States," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    14. 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.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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