Nonparametric and semiparametric estimation with discrete regressors

Juan Mora
Authors registered in the RePEc Author Service: Miguel A. Delgado ()

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: This paper presents and discusses procedures for estimating regression curves when regressors are discrete and applies them to semiparametric inference problems. We show that pointwise root-n-consistency and global consistency of regression curve estimates are achieved without employing any smoothing, even for discrete regressors with unbounded support. These results still hold when smoothers are used, under much weaker conditions than those required with continuous regressors. Such estimates are useful in semiparametric inference problems. We discuss in detail the partially linear regression model and shape-invariant modelling. We also provide some guidance on estimation in semiparametric models where continuous and discrete regressors are present. The paper also includes a Monte Carlo study.

Keywords: Nearest; neighbours; Partially; linear; model; Shape-invariant; modelling; Kernels; Discrete; regressors; Empirical; conditional; expectation; estimate; Regressograms; Nonparametric regression; Semiparametric; inference (search for similar items in EconPapers)
Date: 1994-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://e-archivo.uc3m.es/rest/api/core/bitstreams ... 9190c3737011/content (application/pdf)

Related works:
Journal Article: Nonparametric and Semiparametric Estimation with Discrete Regressors (1995)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:3947

Access Statistics for this paper

More papers in DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística
Bibliographic data for series maintained by Ana Poveda ().