A new method for approximating vector autoregressive processes by finite-state Markov chains
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.
|Date of creation:||08 Jun 2011|
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