Discretization of Highly-Persistent Correlated AR(1) Shocks

Damba Lkhagvasuren and Ragchaasuren Galindev

No 8012, Working Papers from Concordia University, Department of Economics

Abstract: The finite state Markov-Chain approximation method developed by Tauchen (1986) and Tauchen and Hussey (1991) is widely used in economics, finance and econometrics in solving for functional equations where state variables follow an autoregressive process. For highly persistent processes, the method requires a large number of discrete values for the state variables to produce close approximations which leads to an undesirable reduction in computational speed, especially in multidimensional case. This paper proposes an alternative method of discretizing vector autoregressions. The method works well as an approximation and its numerical efficiency applies to a wide range of the parameter space.

Keywords: Finite State Markov-Chain Approximation; Transition Matrix; Numerical Methods; VAR (search for similar items in EconPapers)
JEL-codes: C15 C63 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2008-09, Revised 2008-11
New Economics Papers: this item is included in nep-cba, nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Related works:
Journal Article: Discretization of highly persistent correlated AR(1) shocks (2010)
Working Paper: Discretization of highly persistent correlated AR(1) shocks (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:crd:wpaper:08012

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