Efficient nonparametric three-stage estimation of fixed effects varying coefficient panel data models

This paper is concerned with the estimation of a fixed effects panel data model that adopts a partially linear form, in which the coeffcients of some variables are restricted to be constant but the coeffcients of other variables are assumed to be varying, depending on some exogenous continuous varia...

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Detalles Bibliográficos
Autores: Rodríguez-Poo, Juan M.|||0000-0001-8751-3025, Soberón Velez, Alexandra Pilar|||0000-0001-5268-6751
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/20769
Acceso en línea:http://hdl.handle.net/10902/20769
Access Level:acceso abierto
Palabra clave:Panel data
Endogeneity
Fixed effects
Functional-coeffcient models
Generalized F-test
Instrumental variables
Descripción
Sumario:This paper is concerned with the estimation of a fixed effects panel data model that adopts a partially linear form, in which the coeffcients of some variables are restricted to be constant but the coeffcients of other variables are assumed to be varying, depending on some exogenous continuous variables. Moreover, we allow for the existence of endogeneity in the structural equation. Conditional moment restrictions on first differences are imposed to identify the structural equation. Based on these restrictions we propose a three stage estimation procedure. The asymptotic properties of these proposed estimators are established. Moreover, as a result of the first differences transformation, to estimate the unknown varying coeffcient functions, two alternative backfitting estimators are obtained. As a novelty, we propose a minimum distance estimator that, combining both estimators, is more effcient and achieves the optimal rate of convergence. The feasibility and possible gains of this new procedure are shown by estimating a Life-cycle hypothesis panel data model and a Monte Carlo study is implemented.