rcbr.fit {RCBR} | R Documentation |
Fitting of Random Coefficient Binary Response Models
Description
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.
Usage
rcbr.fit(x, y, offset = NULL, mode = "KW", control)
Arguments
x |
design matrix |
y |
binary response vector |
offset |
specifies a fixed shift in |
mode |
controls whether the Gautier and Kitamura, "GK", or Kiefer and Wolfowitz, "KW" methods are used. |
control |
control parameters for fitting methods
See |
Details
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.
Value
of object of class GK
, KW1
, with components described in
further detail in the respective fitting functions.
Author(s)
Jiaying Gu and Roger Koenker
References
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.