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On Liu Estimators for the Logit Regression Model
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistik)
Department of Mathematics and Statistics, Florida International University, Miami, FL, USA.
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistik)
2012 (English)In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 29, no 4, 1483-1488 p.Article in journal (Refereed) Published
Abstract [en]

This paper introduces a shrinkage estimator for the logit model which is a generalization of the estimator proposed by Liu (1993) for the linear regression. This new estimation method is suggested since the mean squared error (MSE) of the commonly used maximum likelihood (ML) method becomes inflated when the explanatory variables of the regression model are highly correlated. Using MSE, the optimal value of the shrinkage parameter is derived and some methods of estimating it are proposed. It is shown by means of Monte Carlo simulations that the estimated MSE and mean absolute error (MAE) are lower for the proposed Liu estimator than those of the ML in the presence of multicollinearity. Finally the benefit of the Lie estimator is shown in an empirical application where different economic factors are used to explain the probability that municipalities have net increase of inhabitants.

Place, publisher, year, edition, pages
2012. Vol. 29, no 4, 1483-1488 p.
Keyword [en]
Estimation, MAE, MSE, Multicollinearity, Logit, Liu, Simulation
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:hj:diva-20141DOI: 10.1016/j.econmod.2011.11.015Local ID: IHHEFSISOAI: oai:DiVA.org:hj-20141DiVA: diva2:580625
Available from: 2012-12-23 Created: 2012-12-23 Last updated: 2015-11-17Bibliographically approved

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