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

Antonio Pacifico

MPRA Paper from University Library of Munich, Germany

Abstract: This paper aims to address semiparametric forecasting problem when studying high dimensional data in multivariate dynamic panel model with correlated random effects. A hierarchical empirical Bayesian perspective is developed to jointly deal with incidental parameters, structural framework, unobserved heterogeneity, and model misspecification problems. Methodologically, an ad-hoc model selection on a mixture of normal distributions is addressed to obtain the best combination of outcomes to construct empirical Bayes estimator and then investigate ratio-optimality and posterior consistency for better individual–specific forecasts. Simulations for Monte Carlo designs are performed to account for relative regrets dealing with correlated random effects distribution. A real case-study on the current COVID-19 pandemic crisis among a pool of developed and emerging economies is also conducted to highlight the performance of the estimating procedure.

Keywords: Dynamic Panel Data; Ratio-Optimality; Bayesian Methods; Forecasting; MCMC Simulations; Tweedie Correction. (search for similar items in EconPapers)
JEL-codes: C1 C5 O1 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-ecm and nep-for
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https://mpra.ub.uni-muenchen.de/115191/8/MPRA_paper_115191.pdf revised version (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:107523

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