@article{10902/4715,
year = {2003},
url = {http://hdl.handle.net/10902/4715},
abstract = {In this paper we introduce a general method for estimating semiparametrically
the different components in separable models+ The family of separable models
is quite popular in economic research because this structure offers clear interpretation,
has straightforward economic consequences, and is often justified by
theory+ This family is also of statistical interest because it allows us to estimate
high-dimensional complexity semiparametrically without running into the curse
of dimensionality+ We consider even the case when multiple indices appear in the
objective function; thus we can estimate models that are typical in economic analysis,
such as those that contain limited dependent variables+ The idea of the new
method is mainly based on a generalized profile likelihood approach+ Although
this requires some hypotheses on the conditional error distribution, it yields a
quite general usable method with low computational costs but high accuracy even
for small samples+ We give estimation procedures and provide some asymptotic
theory+ Implementation is discussed; simulations and an application demonstrate
its feasibility and good finite-sample behavior.},
publisher = {Cambridge University Press},
publisher = {Econometric Theory, 2003, 19(6), 1008-1039},
title = {Semiparametric estimation of separable models with possibly limited dependent variables},
author = {Rodríguez-Poo, Juan M. and Sperlich, Stefan and Vieu, Philippe},
}