Efficient Likelihood Evaluation of State-Space Representations

We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure employs continuous approximations of filtering densities, and delivers unconditionally optimal global approximations of targeted i...

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Bibliographic Details
Published in:The Review of economic studies 2013-04, Vol.80 (2 (283)), p.538-567
Main Authors: DeJONG, DAVID N., LIESENFELD, ROMAN, MOURA, GUILHERME V., RICHARD, JEAN-FRANÇOIS, DHARMARAJAN, HARIHARAN
Format: Article
Language:English
Subjects:
Approximation
Covariance matrices
Datasets
Density
Economic models
Estimation bias
Filtration
General economic equilibrium
Modeling
Non-linear models
Parameter estimation
Parametric models
Probability theory
Sampling
Standard deviation
Stochastic models
Studies
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Summary:We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure employs continuous approximations of filtering densities, and delivers unconditionally optimal global approximations of targeted integrands to achieve likelihood approximation. Optimized approximations of targeted integrands are constructed via efficient importance sampling. Resulting likelihood approximations are continuous functions of model parameters, greatly enhancing parameter estimation. We illustrate our procedure in applications to dynamic stochastic general equilibrium models.
ISSN:0034-6527
1467-937X
DOI:10.1093/restud/rds040