Finite sample inference for quantile regression models |
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Authors: | Victor Chernozhukov Christian Hansen Michael Jansson |
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Institution: | aMassachusetts Institute of Technology, Department of Economics, United States;bUniversity of Chicago, Graduate School of Business, United States;cUniversity of California - Berkeley, Department of Economics, United States |
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Abstract: | Under minimal assumptions, finite sample confidence bands for quantile regression models can be constructed. These confidence bands are based on the “conditional pivotal property” of estimating equations that quantile regression methods solve and provide valid finite sample inference for linear and nonlinear quantile models with endogenous or exogenous covariates. The confidence regions can be computed using Markov Chain Monte Carlo (MCMC) methods. We illustrate the finite sample procedure through two empirical examples: estimating a heterogeneous demand elasticity and estimating heterogeneous returns to schooling. We find pronounced differences between asymptotic and finite sample confidence regions in cases where the usual asymptotics are suspect. |
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Keywords: | Extremal quantile regression Instrumental quantile regression Partial identification Weak identification |
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