Title
Semiparametric Least Squares (SLS) and Weighted SLS Estimation of Single-Index Models
Publisher
Center for Economic Research, Department of Economics, University of Minnesota
Abstract
For the class of single-index models, I construct a semiparametric estimator of coefficients
up to a multiplicative constant that exhibits 1/ Vn-consistency and asymptotic normality. This
class of models includes censored and truncated Tobit models, binary choice models, and duration
models with unobserved individual heterogeneity and random censoring. I also investigate
a weighting scheme that achieves the semi parametric efficiency bound.
Previously Published Citation
Ichimura, H., (1991), "Semiparametric Least Squares (SLS) and Weighted SLS Estimation of Single-Index Models", Discussion Paper No. 264, Center for Economic Research, Department of Economics, University of Minnesota.
Suggested Citation
Ichimura, Hidehiko.
(1991).
Semiparametric Least Squares (SLS) and Weighted SLS Estimation of Single-Index Models.
Center for Economic Research, Department of Economics, University of Minnesota.
Retrieved from the University of Minnesota Digital Conservancy,
https://hdl.handle.net/11299/55563.