Discretization of highly persistent correlated AR(1) shocks

Ragchassuren Galindev, Damba Lkhagvasuren

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


The finite state Markov-chain approximation methods developed by Tauchen (1986) and Tauchen and Hussey (1991) are widely used in economics, finance and econometrics to solve functional equations in which state variables follow autoregressive processes. For highly persistent processes, the methods require 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 a multivariate case. This paper proposes an alternative method of discretizing multivariate autoregressive processes. This method can be treated as an extension of Rouwenhorst's (1995) method which, according to our finding, outperforms the existing methods in the scalar case for highly persistent processes. The new method works well as an approximation that is much more robust to the number of discrete values for a wide range of the parameter space.
Original languageEnglish
Pages (from-to)1260-1276
Number of pages17
JournalJournal of Economic Dynamics and Control
Issue number7
Publication statusPublished - Jul 2010

ASJC Scopus subject areas

  • Economics and Econometrics
  • Applied Mathematics
  • Control and Optimization


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