A Moment-Matching Method for Approximating Vector Autoregressive Processes by Finite-State Markov Chains
31 Pages Posted: 22 Mar 2015
Date Written: September 2013
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
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