Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/101039
Authors: 
Gospodinov, Nikolay
Lkhagvasuren, Damba
Year of Publication: 
2013
Series/Report no.: 
Working Paper, Federal Reserve Bank of Atlanta 2013-5
Abstract: 
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.
Subjects: 
Markov chain
vector autoregressive processes
numerical methods
moment matching
non-linear stochastic dynamic models state space discretization
stochastic growth model
fiscal policy
JEL: 
C15
C32
C60
E13
E32
E62
Document Type: 
Working Paper

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