Nonparametric classes for identification in random coefficients models when regressors have limited variation

Christophe Gaillac and Eric Gautier

Working Papers from HAL

Abstract: This paper studies point identification of the distribution of the coefficients in some random coefficients models with exogenous regressors when their support is a proper subset, possibly discrete but countable. We exhibit trade-offs between restrictions on the distribution of the random coefficients and the support of the regressors. We consider linear models including those with nonlinear transforms of a baseline regressor, with an infinite number of regressors and deconvolution, the binary choice model, and panel data models such as single-index panel data models and an extension of the Kotlarski lemma.

Keywords: Identification; Random Coefficients; Quasi-analyticity; Deconvolution; Deconvolution AMS 2010 Subject Classification: Primary 62P20; secondary 42A99; 62G07; 62G08 (search for similar items in EconPapers)
Date: 2021-05-21
New Economics Papers: this item is included in nep-dcm and nep-ecm
Note: View the original document on HAL open archive server: https://hal.science/hal-03231392v1
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Working Paper: Non Parametric Classes for Identification in Random Coefficients Models when Regressors have Limited Variation (2021)
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