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An empirical likelihood goodness‐of‐fit test for time series

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  • Song Xi Chen
  • Wolfgang Härdle
  • Ming Li

Abstract

Summary. 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.

Suggested Citation

  • Song Xi Chen & Wolfgang Härdle & Ming Li, 2003. "An empirical likelihood goodness‐of‐fit test for time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(3), pages 663-678, August.
    • Handle: RePEc:bla:jorssb:v:65:y:2003:i:3:p:663-678
    DOI: 10.1111/1467-9868.00408
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    1. Eckhard Platen, 2000. "Risk Premia and Financial Modelling Without Measure Transformation," Research Paper Series 45, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Tripathi, Gautam & Kitamura, Yuichi, 2000. "On testing conditional moment restrictions: The canonical case," SFB 373 Discussion Papers 2000,88, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    3. Hjellvik, Vidar & Yao, Qiwei & Tjostheim, Dag, 1998. "Linearity testing using local polynominal approximation," LSE Research Online Documents on Economics 6638, London School of Economics and Political Science, LSE Library.
    4. Pham, Tuan D. & Tran, Lanh T., 1985. "Some mixing properties of time series models," Stochastic Processes and their Applications, Elsevier, vol. 19(2), pages 297-303, April.
    5. Horowitz, Joel L & Spokoiny, Vladimir G, 2001. "An Adaptive, Rate-Optimal Test of a Parametric Mean-Regression Model against a Nonparametric Alternative," Econometrica, Econometric Society, vol. 69(3), pages 599-631, May.
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