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Comparing distribution functions of errors in linear models: A nonparametric approach

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  • Mora, Juan

Abstract

We describe how to test whether the distribution functions of errors from two linear regression models are the same, with statistics based on empirical distribution functions constructed with residuals. A smooth bootstrap method is used to approximate critical values. Simulations show that the procedure works well in practice.

Suggested Citation

  • Mora, Juan, 2005. "Comparing distribution functions of errors in linear models: A nonparametric approach," Statistics & Probability Letters, Elsevier, vol. 73(4), pages 425-432, July.
    • Handle: RePEc:eee:stapro:v:73:y:2005:i:4:p:425-432
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    References listed on IDEAS

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    1. Koul, H. L. & Lahiri, S. N., 1994. "On Bootstrapping M-Estimated Residual Processes in Multiple Linear-Regression Models," Journal of Multivariate Analysis, Elsevier, vol. 49(2), pages 255-265, May.
    2. Bai, Jushan & Ng, Serena, 2001. "A consistent test for conditional symmetry in time series models," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 225-258, July.
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    Cited by:

    1. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    2. G. I. Rivas-Martínez & M. D. Jiménez-Gamero & J. L. Moreno-Rebollo, 2019. "A two-sample test for the error distribution in nonparametric regression based on the characteristic function," Statistical Papers, Springer, vol. 60(4), pages 1369-1395, August.
    3. Mora, Juan & Mora-López, Llanos, 2010. "Comparing distributions with bootstrap techniques: An application to global solar radiation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 811-819.

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