JSM 2004 - Toronto

Abstract #300636

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Activity Number: 431
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: Business and Economics Statistics Section
Abstract - #300636
Title: Estimation of Partially Additive Linear Models with an Application to Gasoline Demand
Author(s): Dawit Zerom*+ and Sebastiano Manzan
Companies: University of Alberta and University of Amsterdam
Address: School of Business, Edmonton, AB, T6G 2R6, Canada
Keywords: partially additive linear models ; kernel ; gasoline demand ; semiparametric efficient
Abstract:

This paper proposes a kernel-based procedure for the estimation of a partially additive linear model (PALM). The main contribution is the development of a semiparametric efficient estimator for the linear part of the PALM and the derivation of its asymptotic properties. Because the definition of the estimator only involves simple smoother matrices, it is computationally convenient to analyze large datasets. It is also shown that the proposed estimator is asymptotically more efficient than an estimator that ignores the additive structure. A Monte Carlo investigation indicates that the efficiency gain by the proposed estimator is more pronounced when the number of additive nonparametric components is increased; suggesting that the proposed estimator can also be used as a way to deal with the curse of dimensionality problem. The paper includes an application to U.S. gasoline demand using household data (1991 and 1994) from the Residential Transportation Energy Consumption Survey.

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