Document Type

Working Paper

Date

Fall 9-2017

Keywords

ε-Contamination, Hyper g-Priors, Type-II Maximum Likelihood Posterior Density, Panel Data, Robust Bayesian Estimator, Three-Stage Hierarchy.

Language

English

Disciplines

Econometrics | Economic History | Economics | Public Affairs, Public Policy and Public Administration

Description/Abstract

The paper develops a general Bayesian framework for robust linear static panel data models using ε-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects. The ML-II posterior means are weighted averages of the Bayes estimator under a base prior and the data-dependent empirical Bayes estimator. Two-stage and three stage hierarchy estimators are developed and their finite sample performance is investigated through a series of Monte Carlo experiments. These include standard random effects as well as Mundlak-type, Chamberlain-type and Hausman-Taylor-type models. The simulation results underscore the relatively good performance of the three-stage hierarchy estimator. Within a single theoretical framework, our Bayesian approach encompasses a variety of specifications while conventional methods require separate estimators for each case.

ISSN

1525-3066

Additional Information

Working paper no. 208

wp208.pdf (713 kB)
Accessible PDF version

Source

Local input

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Share

COinS