Abstract
We propose a general Monte Carlo simulation technique for evaluating the likelihood function of dynamic latent variables models, based on artificial factorizations of the sequential joint density of the observables and latent variables. The feasibility of the proposed technique is demonstrated by means of a pilot application to a one-parameter disequilibrium model. Extensions to models with weakly exogenous variables and the use of acceleration methods are discussed.
Financial support for this work has been provided by the Ford Foundation, the National Science Foundation (SES-9012202) the Pew Charitable Trust, and by the UK Economic and Social Research Council under Grants B0022012 and R231184. We are pleased to acknowledge useful comments from J. Danielsson and J. Geweke. The usual disclaimer applies.
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Hendry, D.F., Richard, JF. (1992). Likelihood Evaluation for Dynamic Latent Variables Models. In: Amman, H.M., Belsley, D.A., Pau, L.F. (eds) Computational Economics and Econometrics. Advanced Studies in Theoretical and Applied Econometrics, vol 22. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3162-9_1
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DOI: https://doi.org/10.1007/978-94-011-3162-9_1
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