Inconsistency transmission and variance reduction in two-stage quantile regression

Tae-Hwan Kim () and Christophe Muller

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Abstract: In this paper, we propose a new variance reduction method for quantile regressions with endogeneity problems, for alpha-mixing or m-dependent covariates and error terms. First, we derive the asymptotic distribution of two-stage quantile estimators based on the fitted-value approach under very general conditions. Second, we exhibit an inconsistency transmission property derived from the asymptotic representation of our estimator. Third, using a reformulation of the dependent variable, we improve the efficiency of the two-stage quantile estimators by exploiting a tradeoff between an inconsistency confined to the intercept estimator and a reduction of the variance of the slope estimator. Monte Carlo simulation results show the fine performance of our approach. In particular, by combining quantile regressions with first-stage trimmed least-squares estimators, we obtain more accurate slope estimates than 2SLS, 2SLAD and other estimators for a broad set of distributions. Finally, we apply our method to food demand equations in Egypt.

Keywords: Two-stage estimation; Variance reduction; Quantile regression; Asymptotic bias (search for similar items in EconPapers)
Date: 2020-04
New Economics Papers: this item is included in nep-ara and nep-ecm
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-02084505v1
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Published in Communications in Statistics - Simulation and Computation, 2020, 49 (4), pp.1044-1077. ⟨10.1080/03610918.2018.1493505⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02084505

DOI: 10.1080/03610918.2018.1493505

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