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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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  • Nikolay Gospodinov & Damba Lkhagvasuren, 2013. "A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains," FRB Atlanta Working Paper 2013-05, Federal Reserve Bank of Atlanta.
    • Handle: RePEc:fip:fedawp:2013-05
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    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.).
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    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. 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.
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    More about this item

    Keywords

    Markov chain; vector autoregressive processes; numerical methods; moment matching; non-linear stochastic dynamic models state space discretization; stochastic growth model; fiscal policy;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory

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