Nonparametric Estimation in Random Coefficients Binary Choice Models

By Eric Gautier, Y. Kitamura. 2009

105 citations recorded by the source corpus.

This paper considers random coefficients binary choice models. The main goal is to estimate the density of the random coefficients nonparametrically. This is an ill-posed inverse problem characterized by an integral transform. A new density estimator for the random coefficients is developed, utilizing Fourier-Laplace series on spheres. This approach offers a clear insight on the identification problem. More importantly, it leads to a closed form estimator formula that yields a simple plug-in procedure requiring no numerical optimization. The new estimator, therefore, is easy to implement in empirical applications, while being flexible about the treatment of unobserved heterogeneity. Extensions including treatments of non-random coefficients and models with endogeneity are discussed.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/04a5cc0097c63ccc176d5a84438f2a200561b3ca

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Citations in the corpus

Random Coefficients on Endogenous Variables in Simultaneous Equations Models (2014, background): “Ichimura and Thompson (1998), Fox, Kim, Ryan, and Bajari (2012), and Gautier and Kitamura (2013) study binary outcome models with exogenous regressors.”
Nonparametric Welfare Analysis for Discrete Choice (2014, background): “For binary choice, Ichimura and Thompson, 1998 and Gautier and Kitamura, 2013 considered random coe¢ cient models where the dimension of heterogeneity is speci ed to be equal to the number of regressors (plus one for the random intercept) and enter the individual outcome equation in a special way,…”
A SPECTRAL METHOD FOR DECONVOLVING A DENSITY (2010, background): “A similar problem is encountered in random coefficients binary choice models where the distribution of the coefficient is nonparametrically estimated; see Ichimura and Thompson (1998) and Gautier and Kitamura (2008).”
Heterogeneous treatment effects: instrumental variables without monotonicity? (2010, unknown): “Our results extend to all other treatment effect parameters if they can be expressed as functions of the marginal treatment effect, see Heckman and Vytlacil (1999, 2000) for details. By Assumption 2(i) all parameters that are considered here exist. Moreover, they are well defined irrespective of any”
The random coefficients logit model is identified (2012, background): “To fix the undetermined coecients (p) p j’s, we compare the coecients from (5) and ( 8 ) and”