A Two-step System for Hierarchical Bayesian Dynamic Panel Data to deal with Endogeneity Issues, Structural Model Uncertainty, and Causal Relationship

Antonio Pacifico

MPRA Paper from University Library of Munich, Germany

Abstract: The paper develops a computational method implementing a standard Dynamic Panel Data model with Generalized Method of Moment (GMM) estimators to deal with endogeneity issues, structural model uncertainty, and causal relationship in large and long panel databases. The methodology takes the name of Two-step System Dynamic Panel Data, that combines a first-step Bayesian procedure for selecting the only potential predictors in a static linear regression model with a frequentist second-step procedure for estimating the parameters of a dynamic linear panel data model. An empirical example to the effects of obesity, socioeconomic variables, and individual-specific factors on labour market outcomes among Italian regions is performed. Potential prevention policies and strategies to address key behavioural and diseases risk factors affecting labour market outcomes and social environment are also discussed.

Keywords: Bayesian Model Averaging; Dynamic Panel Data; Granger Causality; Labour Market Outcomes; Obesity. (search for similar items in EconPapers)
JEL-codes: C1 D6 I1 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-ecm and nep-ore
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Forthcoming in The European Journal of Health Economics Health Economics.NA(2021): pp. 1-30

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:104291

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