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Estimation of dynamic models with nonparametric simulated maximum likelihood

Kristensen, D; Shin, Y; (2012) Estimation of dynamic models with nonparametric simulated maximum likelihood. Journal of Econometrics , 167 (1) pp. 76-94. 10.1016/j.jeconom.2011.09.042. Green open access

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Abstract

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 jump diffusion models.

Type: Article
Title: Estimation of dynamic models with nonparametric simulated maximum likelihood
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jeconom.2011.09.042
Publisher version: http://dx.doi.org/10.1016/j.jeconom.2011.09.042
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > School of Arts and Social Sciences
URI: http://discovery.ucl.ac.uk/id/eprint/1340726
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