A Moment-Matching Method for Approximating Vector Autoregressive Processes by Finite-State Markov Chains

Nikolay Gospodinov and Damba Lkhagvasuren

No 11005, Working Papers from Concordia University, Department of Economics

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.

Keywords: Markov Chain; Vector Autoregressive Processes; Functional Equation; Numerical Methods; Moment Matching (search for similar items in EconPapers)
JEL-codes: C15 C60 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2011-06-08, Revised 2011-12-16
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (6)

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Related works:
Journal Article: A MOMENT‐MATCHING METHOD FOR APPROXIMATING VECTOR AUTOREGRESSIVE PROCESSES BY FINITE‐STATE MARKOV CHAINS (2014)
Working Paper: A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:crd:wpaper:11005

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