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

31 Pages Posted: 22 Mar 2015

See all articles by Nikolay Gospodinov

Nikolay Gospodinov

Federal Reserve Bank of Atlanta

Damba Lkhagvasuren

Concordia University, Quebec

Date Written: September 2013

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, numerical methods, moment matching, non-linear stochastic dynamic models state space discretization, stochastic growth model, fiscal policy

JEL Classification: C15, C32, C60, E13, E32, E62

Suggested Citation

Gospodinov, Nikolay and Lkhagvasuren, Damba, A Moment-Matching Method for Approximating Vector Autoregressive Processes by Finite-State Markov Chains (September 2013). FRB Atlanta Working Paper 2013-05. Available at SSRN: https://ssrn.com/abstract=2478493 or http://dx.doi.org/10.2139/ssrn.2478493

Nikolay Gospodinov (Contact Author)

Federal Reserve Bank of Atlanta ( email )

Atlanta, GA 30309
United States

HOME PAGE: https://www.frbatlanta.org/research/economists/gospodinov-nikolay.aspx?panel=1

Damba Lkhagvasuren

Concordia University, Quebec ( email )

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