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

Pacifico, Antonio (2021): Semiparametric Forecasting Problem in High Dimensional Dynamic Panel with Correlated Random Effects: A Hierarchical Empirical Bayes Approach. Forthcoming in: Journal of Applied Econometrics , Vol. NA, No. NA : pp. 1-41.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Dynamic Panel Data; Ratio-Optimality; Bayesian Methods; Forecasting; MCMC Simulations; Tweedie Correction.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development

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