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- Title
NONPARAMETRIC ESTIMATION IN RANDOM COEFFICIENTS BINARY CHOICE MODELS.
- Authors
GAUTIER, ERIC; KITAMURA, YUICHI
- Abstract
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 nonrandom coefficients and models with endogeneity are discussed.
- Subjects
NONPARAMETRIC estimation; MULTILEVEL models; DENSITY; INVERSE problems; INTEGRAL transforms; FOURIER series; MATHEMATICAL optimization; HETEROGENEITY
- Publication
Econometrica, 2013, Vol 81, Issue 2, p581
- ISSN
0012-9682
- Publication type
Article
- DOI
10.3982/ECTA8675