Markov-chain approximations of vector autoregressions: Application of general multivariate-normal integration techniques
AbstractDiscrete 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Economics Letters.
Volume (Year): 110 (2011)
Issue (Month): 1 (January)
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Web page: http://www.elsevier.com/locate/ecolet
Markov approximation Non-diagonal Singular covariance;
Other versions of this item:
- Edward S. Knotek II & Stephen Terry, 2008. "Markov-chain approximations of vector autoregressions: application of general multivariate-normal integration techniques," Research Working Paper RWP 08-02, Federal Reserve Bank of Kansas City.
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