Markov-chain approximations of vector autoregressions: Application of general multivariate-normal integration techniques
Stephen Terry () and
Edward Knotek ()
Economics Letters, 2011, vol. 110, issue 1, 4-6
Discrete Markov chains are helpful for approximating vector autoregressive processes in computational work. We relax G. Tauchen (1986) [Finite state Markov-chain approximations to univariate and vector autoregressions. Economics Letters 20, 177-181] in practice using multivariate-normal integration techniques to allow for arbitrary positive-semidefinite covariance structures. Examples are provided for non-diagonal and singular non-diagonal error covariances.
Keywords: Markov; approximation; Non-diagonal; Singular; covariance (search for similar items in EconPapers)
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Working Paper: Markov-chain approximations of vector autoregressions: application of general multivariate-normal integration techniques (2008)
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