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如何应用动态面板自举校正固定效应(xtbcfe)和异质动态(panelhetero)。

How to apply dynamic panel bootstrap-corrected fixed-effects (xtbcfe) and heterogeneous dynamics (panelhetero).

作者信息

Sarkodie Samuel Asumadu, Owusu Phebe Asantewaa

机构信息

Nord University Business School (HHN), Post Box 1490, 8049 Bodø, Norway.

出版信息

MethodsX. 2020 Aug 27;7:101045. doi: 10.1016/j.mex.2020.101045. eCollection 2020.

Abstract

The characteristics of panel data namely, inter alia, missing values, cross-sectional dependence, serial correlation, small time period bias, omitted variable bias, country-specific fixed-effects, time effects, heterogeneous effects and convergence often lead to misspecification, and spurious regression, thus, affecting the consistency and robustness of the model. In this regard, a more sophisticated panel estimation technique that accounts for the attributes and challenges is worthwhile. The novel panel bootstrap-corrected fixed-effects estimator () and heterogeneous dynamics () recommended in this study meets almost all the requirements for robust and consistent panel estimation with an interface for user modifications. We further demonstrate how to use empirical CDF, moments and kernel density estimation to investigate heterogeneous effects. Due to the complexities in the application of and algorithm, we provide a step-by-step procedure and guidelines for the estimation approach. We apply the and algorithm for global estimation of mortality, disability-adjusted life years and welfare cost from exposure to ambient air pollution. Importantly, the algorithm can be applied to any panel data-based studies in social science, environmental science, environmental economics, health economics, energy economics, and among others.•Procedures useful for data imputation and transforming negative variables for time series, cross-sectional and panel data are presented.•Contrary to traditional models, we show how a novel approach can be modified and used to examine the degree of heterogeneous effects across cross-sectional units of panel data.•We demonstrate how the dynamic panel bootstrap-corrected fixed-effects estimator is useful in estimating higher-order panel data models and accounting for challenges such as omitted-variable bias, convergence, cross-section dependence and heterogeneous effects.•We apply the imputation technique, , and algorithms to examine the nexus between ambient air pollution and health outcomes.

摘要

面板数据的特征,尤其是缺失值、横截面依赖性、序列相关性、小时间段偏差、遗漏变量偏差、国家特定固定效应、时间效应、异质性效应和收敛性,常常导致模型设定错误和虚假回归,从而影响模型的一致性和稳健性。在这方面,一种更复杂的考虑到这些属性和挑战的面板估计技术是值得的。本研究推荐的新颖的面板自抽样校正固定效应估计器()和异质性动态()几乎满足稳健且一致的面板估计的所有要求,并提供了用户修改界面。我们进一步展示了如何使用经验累积分布函数、矩和核密度估计来研究异质性效应。由于 和 算法应用中的复杂性,我们提供了估计方法的分步程序和指南。我们应用 和 算法对暴露于环境空气污染导致的死亡率、伤残调整生命年和福利成本进行全局估计。重要的是,该算法可应用于社会科学、环境科学、环境经济学、健康经济学、能源经济学等任何基于面板数据的研究。

• 介绍了对时间序列、横截面和面板数据进行数据插补以及转换负变量的有用程序。

• 与传统模型不同,我们展示了一种新颖的方法如何被修改并用于检验面板数据横截面单位之间的异质性效应程度。

• 我们展示了动态面板自抽样校正固定效应估计器在估计高阶面板数据模型以及应对诸如遗漏变量偏差、收敛性、横截面依赖性和异质性效应等挑战方面的作用。

• 我们应用插补技术、 和 算法来检验环境空气污染与健康结果之间的关系。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15fb/7479353/4a7b552b3e7f/fx1.jpg

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