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Testing non-nested semiparametric models: an application to Engel curves specification

Author

Listed:
  • Miguel A. Delgado

    (Departamento de Estadistica y Econometria, Universidad Carlos III de Madrid, Madrid, Spain)

  • Juan Mora

    (Departamento de Fundamentos del Análisis Económico, Universidad de Alicante, Alicante, Spain)

Abstract

This paper proposes a test statistic for discriminating between two partly non-linear regression models whose parametric components are non-nested. The statistic has the form of a J-test based on a parameter which artificially nests the null and alternative hypotheses. We study in detail the realistic case where all regressors in the non-linear part are discrete and then no smoothing is required on estimating the non-parametric components. We also consider the general case where continuous and discrete regressors are present. The performance of the test in finite samples is discussed in the context of some Monte Carlo experiments. The test is well motivated for specification testing of Engel curves. We provide an application using data from the 1980 Spanish Expenditure Survey. © 1998 John Wiley & Sons, Ltd.

Suggested Citation

  • Miguel A. Delgado & Juan Mora, 1998. "Testing non-nested semiparametric models: an application to Engel curves specification," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(2), pages 145-162.
    • Handle: RePEc:jae:japmet:v:13:y:1998:i:2:p:145-162
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    File URL: http://qed.econ.queensu.ca:80/jae/1998-v13.2/
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    Cited by:

    1. Yuri Fonseca & Marcelo Medeiros & Gabriel Vasconcelos & Alvaro Veiga, 2018. "BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions," Papers 1808.03698, arXiv.org, revised Jul 2020.

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