Estimation of dynamic models with nonparametric simulated maximum likelihood.
Journal of Econometrics
We propose an easy-to-implement simulated maximum likelihood estimator for dynamic models where no closed-form representation of the likelihood function is available. Our method can handle any simulable model without latent dynamics. Using simulated observations, we nonparametrically estimate the unknown density by kernel methods, and then construct a likelihood function that can be maximized. We prove that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically efficient. The higher-order impact of simulations and kernel smoothing on the resulting estimator is also analyzed; in particular, it is shown that the NPSML does not suffer from the usual curse of dimensionality associated with kernel estimators. A simulation study shows good performance of the method when employed in the estimation of jumpdiffusion models. © 2011 Elsevier B.V. All rights reserved.
|Title:||Estimation of dynamic models with nonparametric simulated maximum likelihood|
|UCL classification:||UCL > School of Arts and Social Sciences|
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