RT Generic
T1 Semiparametric estimation of weak and strong separable models
A1 Rodriguez Poo, Juan M.
A1 Sperlich, Stefan
A1 Vieu, Philippe
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB Inthis paper we introduce a general method for estimating semiparametrically the differentcomponents in weak or strong separable models. The family of separable models is quitepopular in economic theory and empirical research as this structure offers clear interpretation,has straight forward economic consequences and often is justified by theory. As will be seen inthis article they are also of statistical interest since they allow to estimate semiparametricallyhigh dimensional complexity without running in the so called curse of dimensionality.Generalized additive models and generalized partial linear models are special cases in this familyof models. The idea of the new method is mainly based on a combination of local likelihood andefficient estimators in non or semiparametric models. Although this imposes some hypothesis onthe error distribution this yields a very general usable method with little computational costs andhigh exactness even for small samples. E. g. it enables us to include models for censored andtruncated variables which are quite common in quantitative economics. We give the estimationprocedures and provide asymptotic theory for them. Implementation is discussed, simulationsand an application demonstrate its feasibility in finite sample behavior
YR 2000
FD 2000-10
LK https://hdl.handle.net/10016/10064
UL https://hdl.handle.net/10016/10064
LA eng
DS e-Archivo
RD 20 may. 2024