Kernel Likelihood Inference for Time Series |
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Authors: | CARLO GRILLENZONI |
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Affiliation: | Faculty of Planning, University IUAV of Venice |
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Abstract: | Abstract. 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. |
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Keywords: | adaptive estimation ARMAX models cross validation dual likelihood empirical likelihood kernel density martingale difference |
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