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El contraste reset en los modelos logit y probit. Un estudio de Monte Carlo

Author

Listed:
  • Jos� Antonio P�rez M�nguez

    (Universidad de Zaragoza)

  • Inmaculada Villan�a Mart�n

    (Universidad de Zaragoza)

Abstract

En este trabajo se analiza el comportamiento del contraste Reset en los modelos binarios probit y logit. Este es un contraste general de especificaci�n ampliamente utilizado en modelos lineales cl�sicos, que permite captar algunos errores cometidos en nuestras modelizaciones. A trav�s de simulaciones se estudia su funcionamiento en estimaciones probit y logit, tanto si la especificaci�n es correcta como si existen errores por variables omitidas, heterocedasticidad o incorrecta funci�n de distribuci�n de la perturbaci�n. Los resultados muestran un buen comportamiento del contraste cuando el modelo est� correctamente especificado, y nos permiten conocer la sensibilidad del contraste ante determinados errores.

Suggested Citation

  • Jos� Antonio P�rez M�nguez & Inmaculada Villan�a Mart�n, 2022. "El contraste reset en los modelos logit y probit. Un estudio de Monte Carlo," Documentos de Trabajo dt2022-02, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    • Handle: RePEc:zar:wpaper:dt2022-02
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    More about this item

    Keywords

    variables omitidas; heterocedasticidad; error de especificaci�n; tama�o; potencia.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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