Semiparametric estimation of structural functions in nonseparable triangular models

Victor Chernozhukov, Ivan Fernandez-Val (), Whitney Newey, Sami Stouli and Francis Vella

No CWP48/17, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: This paper introduces two classes of semiparametric triangular systems with nonadditively separable unobserved heterogeneity. They are based on distribution and quantile regression modeling of the reduced-form conditional distributions of the endogenous variables. We show that these models are flexible and identify the average, distribution and quantile structural functions using a control function approach that does not require a large support condition. We propose a computationally attractive three-stage procedure to estimate the structural functions where the first two stages consist of quantile or distribution regressions. We provide asymptotic theory and uniform inference methods for each stage. In particular, we derive functional central limit theorems and bootstrap functional central limit theorems for the distribution regression estimators of the structural functions. We illustrate the implementation and applicability of our methods with numerical simulations and an empirical application to demand analysis.

Keywords: Structural functions; nonseparable models; control function; quantile and distribution regression; semiparametric estimation; uniform inference (search for similar items in EconPapers)
Date: 2017-11-08
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Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Semiparametric estimation of structural functions in nonseparable triangular models (2020)
Working Paper: Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models (2019)
Working Paper: Semiparametric estimation of structural functions in nonseparable triangular models (2017)
Working Paper: Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models (2017)
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