Premium
Kernel Likelihood Inference for Time Series
Author(s) -
GRILLENZONI CARLO
Publication year - 2009
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2008.00617.x
Subject(s) - mathematics , empirical likelihood , kernel smoother , kernel density estimation , estimator , statistical inference , inference , variable kernel density estimation , kernel regression , likelihood function , kernel (algebra) , statistics , kernel method , maximum likelihood , artificial intelligence , computer science , radial basis function kernel , discrete mathematics , support vector machine
. 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.