Author(s)

Tong Zeng¹, R. Carter Hill²

¹Department of Applied Business Sciences and Economics, University of La Verne, La Verne, CA, USA.
²Department of Economics, Louisiana State University, Baton Rouge, LA, USA.

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.

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

Zeng, T. and Hill, R. (2016) Shrinkage Estimation in the Random Parameters Logit Model. Open Journal of Statistics, 6, 667-674. doi: 10.4236/ojs.2016.64056.