A new method for approximating vector autoregressive processes by finite-state Markov chains
AbstractThis 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 33827.
Date of creation: 08 Jun 2011
Date of revision:
Markov Chain; Vector Autoregressive Processes; Functional Equation; Numerical Methods; Moment Matching; Numerical Integration;
Find related papers by JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-10-15 (All new papers)
- NEP-ECM-2011-10-15 (Econometrics)
- NEP-ETS-2011-10-15 (Econometric Time Series)
- NEP-ORE-2011-10-15 (Operations Research)
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