Applied Statistics and Econometrics, University of Macerata, Macerata, Italy.
Eur J Health Econ. 2023 Jun;24(4):557-574. doi: 10.1007/s10198-022-01493-3. Epub 2022 Jul 22.
This paper investigates the effects of obesity, socio-economic variables, and individual-specific factors on work productivity across Italian regions. A dynamic panel data with correlated random effects is used to jointly deal with incidental parameters, endogeneity issues, and functional forms of misspecification. Methodologically, a hierarchical semiparametric Bayesian approach is involved in shrinking high dimensional model classes, and then obtaining a subset of potential predictors affecting outcomes. Monte Carlo designs are addressed to construct exact posterior distributions and then perform accurate forecasts. Cross-sectional Heterogeneity is modelled nonparametrically allowing for correlation between heterogeneous parameters and initial conditions as well as individual-specific regressors. Prevention policies and strategies to handle health and labour market prospects are also discussed.
本文研究了肥胖、社会经济变量和个体特征因素对意大利各地区工作生产力的影响。采用具有相关随机效应的动态面板数据来共同处理偶然参数、内生性问题和函数形式的误设定。在方法上，使用分层半参数贝叶斯方法来缩小高维模型类，并从中选择影响结果的潜在预测因子子集。通过蒙特卡罗设计来构建精确的后验分布，从而进行准确的预测。通过非参数方法对横截面异质性进行建模，允许异质参数和初始条件以及个体特定回归量之间存在相关性。还讨论了预防政策和策略，以应对健康和劳动力市场前景。
