Please use this identifier to cite or link to this item: https://doi.org/10.1111/1467-9868.00408
Title: An empirical likelihood goodness-of-fit test for time series
Authors: Chen, S.X. 
Härdle, W.
Li, M. 
Keywords: α-mixing
Empirical likelihood
Goodness-of-fit test
Nadaraya-Watson estimator
Parametric models
Power of test
Square-root processes
Weak dependence
Issue Date: 2003
Citation: Chen, S.X., Härdle, W., Li, M. (2003). An empirical likelihood goodness-of-fit test for time series. Journal of the Royal Statistical Society. Series B: Statistical Methodology 65 (3) : 663-678. ScholarBank@NUS Repository. https://doi.org/10.1111/1467-9868.00408
Abstract: Standard goodness-of-fit tests for a parametric regression model against a series of nonparametric alternatives are based on residuals arising from a fitted model. When a parametric regression model is compared with a nonparametric model, goodness-of-fit testing can be naturally approached by evaluating the likelihood of the parametric model within a nonparametric framework. We employ the empirical likelihood for an α-mixing process to formulate a test statistic that measures the goodness of fit of a parametric regression model. The technique is based on a comparison with kernel smoothing estimators. The empirical likelihood formulation of the test has two attractive features. One is its automatic consideration of the variation that is associated with the nonparametric fit due to empirical likelihood's ability to Studentize internally. The other is that the asymptotic distribution of the test statistic is free of unknown parameters, avoiding plug-in estimation. We apply the test to a discretized diffusion model which has recently been considered in financial market analysis.
Source Title: Journal of the Royal Statistical Society. Series B: Statistical Methodology
URI: http://scholarbank.nus.edu.sg/handle/10635/113920
ISSN: 13697412
DOI: 10.1111/1467-9868.00408
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