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A MOMENT‐MATCHING METHOD FOR APPROXIMATING VECTOR AUTOREGRESSIVE PROCESSES BY FINITE‐STATE MARKOV CHAINS
Author(s) -
Gospodinov Nikolay,
Lkhagvasuren Damba
Publication year - 2014
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2354
Subject(s) - markov chain , autoregressive model , moment (physics) , mathematics , finite state , matching (statistics) , state space , range (aeronautics) , markov property , variable order markov model , markov process , markov kernel , multivariate statistics , state vector , mathematical optimization , markov model , computer science , econometrics , statistics , physics , materials science , classical mechanics , composite material
SUMMARY 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.