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.
JEL classification: C15, C32, C60, E13, E32, E62
Key words: Markov chain, vector autoregressive processes, numerical methods, moment matching, non-linear stochastic dynamic models state space discretization, stochastic growth model, fiscal policy
The authors thank the coeditor Fabio Canova, three anonymous referees, Yongsung Chang, Paul Gomme, Andrei Jirnyi, Paul Klein, and Owen Wu for helpful comments and suggestions. The views expressed here are the authors' and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors' responsibility.
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