Nonparametric Estimation of a Generalized Additive Model with an Unknown Link Function
AbstractThis paper is concerned with estimating the mean of a random variable Y conditional on a vector of covariates X under weak assumptions about the form of the conditional mean function. Fully nonparametric estimation is usually unattractive when X is multidimensional because estimation precision decreases rapidly as the dimension of X increases. This problem can be overcome by using dimension reduction methods such as single-index, additive, multiplicative, and partially linear models. These models are non-nested, however, so an analyst must choose among them. If an incorrect choice is made, the resulting model is misspecified and inferences based on it may be misleading. This paper describes an estimator for a new model that nests single-index, additive, and multiplicative models. The new model achieves dimension reduction without the need for choosing between single-index, additive, and multiplicative specifications. The centered, normalized estimators of the new model's unknown functions are asymptotically normally distributed. An extension of the new model nests partially linear models
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Bibliographic InfoPaper provided by University of Iowa, Department of Economics in its series Working Papers with number 98-05.
Length: 42 Pages
Date of creation: Jul 1998
Date of revision:
Contact details of provider:
Postal: University of Iowa, Department of Economics, Henry B. Tippie College of Business, Iowa City, Iowa 52242
Phone: (319) 335-0829
Fax: (319) 335-1956
Web page: http://tippie.uiowa.edu/economics/
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Nonparametric regression; dimension reduction; kernel estimation; single-index model; partially linear model;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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