A MOMENT‐MATCHING METHOD FOR APPROXIMATING VECTOR AUTOREGRESSIVE PROCESSES BY FINITE‐STATE MARKOV CHAINS

Nikolay Gospodinov and Damba Lkhagvasuren

Journal of Applied Econometrics, 2014, vol. 29, issue 5, 843-859

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.

Date: 2014
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Related works:
Working Paper: A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains (2013)
Working Paper: A Moment-Matching Method for Approximating Vector Autoregressive Processes by Finite-State Markov Chains (2011)
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