TITLE:
Shrinkage Estimation in the Random Parameters Logit Model
AUTHORS:
Tong Zeng, R. Carter Hill
KEYWORDS:
Pretest Estimator, Stein-Rule Estimator, Positive-Part Stein-Like Estimator, Likelihood Ratio Test, Random Parameters Logit Model
JOURNAL NAME:
Open Journal of Statistics,
Vol.6 No.4,
August
23,
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.