S-WoPEc
 
Scandinavian Working Papers in Economics
HomeAboutSeriesSubject/JEL codesAdvanced Search
HUI Research HUI Working Papers, HUI Research

No 51:
New Liu Estimators for the Poisson Regression Model: Method and Application

Kristofer Månsson (), B. M. Golam Kibria, Pär Sjölander and Ghazi Shukur

Abstract: A new shrinkage estimator for the Poisson model is introduced in this paper. This method is a generalization of the Liu (1993) estimator originally developed for the linear regression model and will be generalised here to be used instead of the classical maximum likelihood (ML) method in the presence of multicollinearity since the mean squared error (MSE) of ML becomes inflated in that situation. Furthermore, this paper derives the optimal value of the shrinkage parameter and based on this value some methods of how the shrinkage parameter should be estimated are suggested. Using Monte Carlo simulation where the MSE and mean absolute error (MAE) are calculated it is shown that when the Liu estimator is applied with these proposed estimators of the shrinkage parameter it always outperforms the ML. Finally, an empirical application has been considered to illustrate the usefulness of the new Liu estimators.

Keywords: Estimation; MSE; MAE; Multicollinearity; Poisson; Liu; Simulation; (follow links to similar papers)

JEL-Codes: C53; (follow links to similar papers)

11 pages, June 30, 2011

Before downloading any of the electronic versions below you should read our statement on copyright.
Download GhostScript for viewing Postscript files and the Acrobat Reader for viewing and printing pdf files.

Full text versions of the paper:

MediaBinaryLoader.axd?Med ... rchive_ForceDownload=true    PDF-file
Download Statistics

Questions (including download problems) about the papers in this series should be directed to Helena Nilsson ()
Report other problems with accessing this service to Sune Karlsson () or Helena Lundin ().

Programing by
Design by Joachim Ekebom

Handle: RePEc:hhs:huiwps:0051 This page was generated on 2016-03-01 15:54:32