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

Gospodinov, Nikolay and Lkhagvasuren, Damba (2011): A new method for approximating vector autoregressive processes by finite-state Markov chains.

Abstract

This paper proposes a new method for approximating vector autoregressions 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
• 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 - General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C60 - General

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