We propose a simulated maximum likelihood estimator for dynamic models based on non-parametric kernel methods. Our … method is designed for models without latent dynamics from which one can simulate observations but cannot obtain a closed … dynamic models that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically …

models. This method is based on the subsampling theory proposed by Politis and Romano (1992, 1994) which computes an …

A simulation method based on importance sampling, Gibbs and Metropolis-Hastings techniques allows to approximate the … maximum likelihood estimator in the general framework of dynamic latent variable models. Some examples of this class of models … are factor models, switching regime models, dynamic limited dependent varaible models, stochastic volatility models and …

Many common statistical models can be specified as linear models with restrictions imposed on the parameters. A large … amount of these models impose restrictions which do not allow for the analytical construction of the probability density …

In this paper we discuss parameter identification and likelihood evaluation for multinomial multiperiod Probit models …