EconPapers    
Economics at your fingertips  
 

Weighted-average least squares estimation of generalized linear models

Giuseppe De Luca (), Jan R. Magnus and Franco Peracchi ()

Journal of Econometrics, 2018, vol. 204, issue 1, 1-17

Abstract: The weighted-average least squares (WALS) approach, introduced by Magnus et al. (2010) in the context of Gaussian linear models, has been shown to enjoy important advantages over other strictly Bayesian and strictly frequentist model-averaging estimators when accounting for problems of uncertainty in the choice of the regressors. In this paper we extend the WALS approach to deal with uncertainty about the specification of the linear predictor in the wider class of generalized linear models (GLMs). We study the large-sample properties of the WALS estimator for GLMs under a local misspecification framework, and the finite-sample properties of this estimator by a Monte Carlo experiment the design of which is based on a real empirical analysis of attrition in the first two waves of the Survey of Health, Aging and Retirement in Europe (SHARE).

Keywords: WALS; Model averaging; Generalized linear models; Monte Carlo; Attrition (search for similar items in EconPapers)
JEL-codes: C51 C25 C13 C11 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407618300034
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Weighted-average least squares estimation of generalized linear models (2017) Downloads
Working Paper: Weighted-Average Least Squares Estimation of Generalized Linear Models (2017) Downloads
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:eee:econom:v:204:y:2018:i:1:p:1-17

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2019-04-15
Handle: RePEc:eee:econom:v:204:y:2018:i:1:p:1-17