Finite State Markov-Chain Approximations to Highly Persistent Processes Report as inadecuate




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Abstract

This paper re-examines the Rouwenhorst method of approximating first-order autoregressive processes. This method is appealing because it can match the conditional and unconditional mean, the conditional and unconditional variance and the first-order autocorrelation of any AR1 process. This paper provides the first formal proof of this and other results. When comparing to five other methods, the Rouwenhorst method has the best performance in approximating the business cycle moments generated by the stochastic growth model. It is shown that, equipped with the Rouwenhorst method, an alternative approach to generating these moments has a higher degree of accuracy than the simulation method.



Item Type: MPRA Paper -

Original Title: Finite State Markov-Chain Approximations to Highly Persistent Processes-

Language: English-

Keywords: Numerical Methods; Finite State Approximations; Optimal Growth Model-

Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling-





Author: Kopecky, Karen A.

Source: https://mpra.ub.uni-muenchen.de/15122/







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