Shrinkage Estimation in the Random Parameters Logit Model

Open Journal of Statistics, 2016, 6, 667-674

Posted: 9 Sep 2016

See all articles by Tong Zeng

Tong Zeng

University of La Verne-Department of Applied Business Sciences and Economics

Carter Hill

Louisiana State University, Baton Rouge - Department of Economics

Date Written: August 30, 2016

Abstract

In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model estimator in the Random Parameters Logit (RPL) model. The results of our Monte Carlo experiments show that the positive-part Stein-like estimator provides smaller MSE than the pretest estimator in the fully correlated RPL model. Both of them outperform the fully correlated RPL model estimator and provide more accurate information on the share of population putting a positive or negative value on the alternative attributes than the fully correlated RPL model estimates. The Monte Carlo mean estimates of direct elasticity with pretest and positive-part Stein-like estimators are closer to the true value and have smaller standard errors than those with fully correlated RPL model estimator.

Keywords: Pretest Estimator, Stein-Rule Estimator, Positive-Part Stein-Like Estimator, Likelihood Ratio Test, Random Parameters Logit Model

JEL Classification: C02, C13, C15, C25

Suggested Citation

Zeng, Tong and Hill, R. Carter, Shrinkage Estimation in the Random Parameters Logit Model (August 30, 2016). Open Journal of Statistics, 2016, 6, 667-674, Available at SSRN: https://ssrn.com/abstract=2836339

Tong Zeng (Contact Author)

University of La Verne-Department of Applied Business Sciences and Economics ( email )

1950 Third Street
La Verne, CA 91750
United States

R. Carter Hill

Louisiana State University, Baton Rouge - Department of Economics ( email )

Department of economics
Baton Rouge, LA 70803-6308
United States

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