The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Semiparametric Estimation

  • James L. Powell
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_1676

Abstract

Semiparametric estimation methods are used for models which are partly parametric and partly nonparametric; typically the parametric part is an underlying regression function which is assumed to be linear in the observable explanatory variables, while the nonparametric component involves the distribution of the model’s ‘error terms’. Semiparametric methods are particularly useful for limited dependent variable models (for example, the binary response or censored regression models), since fully parametric specifications for those models yield inconsistent estimators if the parametric distribution of the errors is misspecified.

Keywords

Binary response models Censored regression (‘Tobit’) models Fixed effects Identification Kernel estimators Limited dependent variable models Linear regression models Maximum likelihood Maximum score estimation Nonparametric estimation Panel data models Propensity score Sample selection models Selectivity bias Semiparametric estimation Semiparametric regression models 

JEL Classifications

C14 
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© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • James L. Powell
    • 1
  1. 1.