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Testing Additivity in Generalized Nonparametric Regression Models

Author

Listed:
  • Linton, O.
  • Gozalo, P.

Abstract

We propose a nonparametric test of conditional independence based on the empirical distribution function. The asymptotic null distribution is a mixture of chi-squares. A bootstrap procedure is proposed for calculating the critical values. Our test has power against alternatives at distance n^{-1/2} from the null. Monte Carlo simulations provide evidence on size and power. We apply the test to the Boston housing dataset.
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Suggested Citation

  • Linton, O. & Gozalo, P., 1996. "Testing Additivity in Generalized Nonparametric Regression Models," SFB 373 Discussion Papers 1996,47, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:199647
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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