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

This paper proposes a new method for approximating vector autoregressions by a finite-state Markov chain. The method is more robust to the number of discrete values and tends to outperform the existing methods 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:	2011-06-08
Authors:	Gospodinov, Nikolay ; Lkhagvasuren, Damba
Institutions:	Volkswirtschaftliche Fakultät, Ludwig-Maximilians-Universität München
Subject:	Markov Chain \| Vector Autoregressive Processes \| Functional Equation \| Numerical Methods \| Moment Matching \| Numerical Integration

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Extent:	application/pdf
Series:	MPRA Paper.
Type of publication:	Book / Working Paper
Classification:	C10 - Econometric and Statistical Methods: General. General ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C60 - Mathematical Methods and Programming. General
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10009323644