Shrinkage Estimation in the Random Parameters Logit Model
Open Journal of Statistics, 2016, 6, 667-674
Posted: 9 Sep 2016
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
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