Kernel Likelihood Inference for Time Series
This paper develops non-parametric techniques for dynamic models whose data have unknown probability distributions. Point estimators are obtained from the maximization of a semiparametric likelihood function built on the kernel density of the disturbances. This approach can also provide Kullback-Leibler cross-validation estimates of the bandwidth of the kernel densities. Confidence regions are derived from the dual-empirical likelihood method based on non-parametric estimates of the scores. Limit theorems for martingale difference sequences support the statistical theory; moreover, simulation experiments and a real case study show the validity of the methods. Copyright (c) 2008 Board of the Foundation of the Scandinavian Journal of Statistics.
Volume (Year): 36 (2009)
Issue (Month): 1 ()
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