A MOMENT-MATCHING METHOD FOR APPROXIMATING VECTOR AUTOREGRESSIVE PROCESSES BY FINITE-STATE MARKOV CHAINS
Article first published online: 25 SEP 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Journal of Applied Econometrics
Volume 29, Issue 5, pages 843–859, August 2014
How to Cite
2014), A MOMENT-MATCHING METHOD FOR APPROXIMATING VECTOR AUTOREGRESSIVE PROCESSES BY FINITE-STATE MARKOV CHAINS, Journal of Applied Econometrics, 29, pages 843–859, doi: 10.1002/jae.2354and (
- Issue published online: 29 JUL 2014
- Article first published online: 25 SEP 2013
- Manuscript Revised: 25 JUL 2013
- Manuscript Received: 10 SEP 2012
This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and tends to outperform the existing methods for approximating multivariate processes over a wide range of the parameter space, especially for highly persistent vector autoregressions with roots near the unit circle. Copyright © 2013 John Wiley & Sons, Ltd.