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Robust Linear Static Panel Data Models Using ε-Contamination

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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.

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  • Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2017. "Robust Linear Static Panel Data Models Using ε-Contamination," Center for Policy Research Working Papers 208, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:208
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    File URL: https://surface.syr.edu/cpr/239/
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    References listed on IDEAS

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    20. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
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    Cited by:

    1. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2021. "Robust Dynamic Panel Data Models Using 𝛆𝛆-Contamination," Center for Policy Research Working Papers 240, Center for Policy Research, Maxwell School, Syracuse University.
    2. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2022. "Robust Dynamic Panel Data Models Usingε-Contamination," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, volume 43, pages 307-336, Emerald Group Publishing Limited.
    3. Anoop Chaturvedi & Shalabh & Sandeep Mishra, 2021. "Generalized Bayes Estimator for Spatial Durbin Model," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 267-285, December.
    4. Olatunji Abdul Shobande, 2021. "Decomposing the Persistent and Transitory Effect of Information and Communication Technology on Environmental Impacts Assessment in Africa: Evidence from Mundlak Specification," Sustainability, MDPI, vol. 13(9), pages 1-12, April.
    5. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2022. "Robust Dynamic Space-Time Panel Data Models Using ε-contamination: An Application to Crop Yields and Climate Change," Center for Policy Research Working Papers 254, Center for Policy Research, Maxwell School, Syracuse University.
    6. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    7. Ponomarev, Yuriy (Пономарев, Юрий) & Rey, Aleksey (Рей, Алексей) & Radchenko, Darya (Радченко, Дарья), 2018. "Investigation of the Relationship between the Intensity of International Trade and the Volatility of Paired Exchange Rates of the Russian Federation and its Trading Partners [Исследование Взаимосвя," Working Papers 061823, Russian Presidential Academy of National Economy and Public Administration.
    8. Shobande, Olatunji A., 2023. "Rethinking social change: Does the permanent and transitory effects of electricity and solid fuel use predict health outcome in Africa?," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).

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    More about this item

    Keywords

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

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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