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- W3121736260 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." @default.
- W3121736260 created "2021-02-01" @default.
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- W3121736260 date "2012-03-01" @default.
- W3121736260 modified "2023-10-18" @default.
- W3121736260 title "Estimation of dynamic models with nonparametric simulated maximum likelihood" @default.
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- W3121736260 doi "https://doi.org/10.1016/j.jeconom.2011.09.042" @default.
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