Markov-chain approximations of vector autoregressions: application of general multivariate-normal integration techniques
Edward Knotek () and
Stephen Terry ()
No RWP 08-02, Research Working Paper from Federal Reserve Bank of Kansas City
Discrete Markov chains can be useful to approximate vector autoregressive processes for economists doing computational work. One such approximation method first presented by Tauchen (1986) operates under the general theoretical assumption of a transformed VAR with diagonal covariance structure for the process error term. We demonstrate one simple method of more conveniently treating this approximation problem in practice using readily available multivariate-normal integration techniques to allow for arbitrary positive-semidefinite covariance structures. Examples are provided using processes with non-diagonal and singular non-diagonal error covariances.
Date: 2008, Revised 2008
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Journal Article: Markov-chain approximations of vector autoregressions: Application of general multivariate-normal integration techniques (2011)
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