A new method for approximating vector autoregressive processes by finite-state Markov chains Reportar como inadecuado




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Abstract

This paper proposes a new method for approximating vectorautoregressions by a finite-state Markov chain. The method is more robust to the number of discrete values and tends to outperform the existing methods over a wide range of the parameter space, especially for highly persistent vector autoregressions with roots near the unit circle.



Item Type: MPRA Paper -

Original Title: A new method for approximating vector autoregressive processes by finite-state Markov chains-

Language: English-

Keywords: Markov Chain, Vector Autoregressive Processes, Functional Equation, Numerical Methods, Moment Matching, Numerical Integration-

Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C10 - GeneralC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: GeneralC - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C60 - General-





Autor: Gospodinov, Nikolay

Fuente: https://mpra.ub.uni-muenchen.de/33827/







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