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High Dimensional Dynamic Panel with Correlated Random Effects: A Semiparametric Hierarchical Empirical Bayes Approach

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  • Pacifico, Antonio

Abstract

A novel for multivariate dynamic panel data analysis with correlated random effects is proposed when estimating high dimensional parameter spaces. A semiparametric hierarchical Bayesian strategy is used to jointly deal with incidental parameters, endogeneity issues, and model misspecification problems. The underlying methodology involves addressing an \texttt{ad-hoc} model selection based on conjugate informative proper mixture priors to select promising subsets of predictors affecting outcomes. Monte Carlo algorithms are then conducted on the resulting submodels to construct empirical Bayes estimators and investigate ratio-optimality and posterior consistency for forecasting purposes and policy issues. An empirical approach to a large panel of economies is conducted describing the functioning of the model. Simulations based on Monte Carlo designs are also performed to account for relative regrets dealing with cross-sectional heterogeneity.

Suggested Citation

  • Pacifico, Antonio, 2021. "High Dimensional Dynamic Panel with Correlated Random Effects: A Semiparametric Hierarchical Empirical Bayes Approach," MPRA Paper 115711, University Library of Munich, Germany, revised 2022.
    • Handle: RePEc:pra:mprapa:115711
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    More about this item

    Keywords

    Multidimensional data; Bayesian Inference; Conditional Forecasting; Incidental Parameters; Tweedie Correction; Multicountry Analysis.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

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