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A Robust Test of Exogeneity Based on Quantile Regressions

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Abstract

In this paper, we propose a robust test of exogeneity. The test statistics is constructed from quantile regression estimators, which are robust to heavy tails of errors. We derive the asymptotic distribution of the test statistic under the null hypothesis of exogeneity at a given quantile. Then, the finite sample properties of the test are investigated through Monte Carlo simulations that exhibit not only good size and power properties, but also good robustness to outliers.

Suggested Citation

  • Tae-Hwan Kim & Christophe Muller, 2017. "A Robust Test of Exogeneity Based on Quantile Regressions," AMSE Working Papers 1716, Aix-Marseille School of Economics, France.
    • Handle: RePEc:aim:wpaimx:1716
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    Cited by:

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    2. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 123-128, August.

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    More about this item

    Keywords

    regression quantile; endogeneity; Two-Stage Estimation; Hausman test;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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