A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains

Nikolay Gospodinov and Damba Lkhagvasuren

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.

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Year of publication:	2013
Authors:	Gospodinov, Nikolaj ; Lkhagvasuren, Damba
Publisher:	Atlanta, Ga. : Federal Reserve Bank of Atlanta
Subject:	Markov chain \| vector autoregressive processes \| numerical methods \| moment matching \| non-linear stochastic dynamic models state space discretization \| stochastic growth model \| fiscal policy \| Markov-Kette \| VAR-Modell \| VAR model \| Stochastisches Wachstumsmodell \| Stochastic growth model \| Zeitreihenanalyse \| Time series analysis \| Finanzpolitik \| Fiscal policy \| Schätzung \| Estimation \| Stochastischer Prozess \| Stochastic process \| Schätztheorie \| Estimation theory