Empirical Best Prediction Under Unit-Level Logit Mixed Models

By Tomás Hobza, Domingo Morales. 2016

60 citations recorded by the source corpus.

Abstract The article applies unit-level logit mixed models to estimating small-area weighted sums of probabilities. The model parameters are estimated by the method of simulated moments (MSM). The empirical best predictor (EBP) of weighted sums of probabilities is calculated and compared with plug-in estimators. An approximation to the mean-squared error (MSE) of the EBP is derived and a bias-corrected MSE estimator is given and compared with parametric bootstrap alternatives. Some simulation experiments are carried out to study the empirical behavior of the model parameter MSM estimators, the EBP and plug-in estimators and the MSE estimators. An application to the estimation of poverty proportions in the counties of the region of Valencia, Spain, is given.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fc8600adee32e18c303bea5bf7a2b7f353e20993

Citations in the corpus

• Small area estimation of poverty proportions under unit-level temporal binomial-logit mixed models (2017, unknown): “We also revisit the application given by Hobza and Morales (2016) to data from the Spanish living conditions survey (SLCS) of 2012.”