Nonparametric approaches to generalized linear models

Wolfgang Härdle and Berwin Turlach

No 1992037, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: In this paper we investigate the gains of using nonparametric estimation methods in a family of models related to Generalised Linear Models. We focus especially on discrete choice models. We give an overview on different nonparametric and semiparametric approaches in this setting. In particular we discuss estimation methods such as average derivative estimation (ADE), semiparametric weighted least squares (Single Index Models, SIM), Projection Pursuit Regression (PPR) and Generalized Additive Models (GAM). Their performance in practice and theory is compared.

Date: 1992-07-01
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