Two methods are implemented for estimating binary response models with random coefficients: A nonparametric maximum likelihood method proposed by Cosslett (1986) and extended by Ichimura and Thompson (1998), and a (hemispherical) deconvolution method proposed by Gautier and and Kitamura (2013). The former is closely related to the NPMLE for mixture models of Kiefer and Wolfowitz (1956). The latter is an R translation of the matlab implementation of Gautier and Kitamura.
rcbr.fit(x, y, offset = NULL, mode = "KW", control)of object of class GK, KW1, with components described in
further detail in the respective fitting functions.
design matrix
binary response vector
specifies a fixed shift in v representing the
potential effect of other covariates having fixed coefficients that may be
useful for profile likelihood computations. (Should be vector of the same
length as v.
controls whether the Gautier and Kitamura, "GK", or Kiefer and Wolfowitz, "KW" methods are used.
control parameters for fitting methods
See GK.control and KW.control for further details.
Jiaying Gu and Roger Koenker
The predict method produces estimates of the probability of a "success"
(y = 1) for a particular vector, (z,v), when aggregated over the estimated
distribution of random coefficients.
Kiefer, J. and J. Wolfowitz (1956) Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters, Ann. Math. Statist, 27, 887-906.
Cosslett, S. (1983) Distribution Free Maximum Likelihood Estimator of the Binary Choice Model, Econometrica, 51, 765-782. Gautier, E. and Y. Kitamura (2013) Nonparametric estimation in random coefficients binary choice models, Ecoonmetrica, 81, 581-607.
Groeneboom, P. and K. Hendrickx (2016) Current Status Linear Regression, preprint available from https://arxiv.org/abs/1601.00202.
Ichimuma, H. and T. S. Thompson, (1998) Maximum likelihood estimation of a binary choice model with random coefficients of unknown distribution," Journal of Econometrics, 86, 269-295.