WHAT TO DO (AND NOT TO DO) WITH TIME-SERIES CROSS-SECTION DATA
WHAT TO DO (AND NOT TO DO) WITH TIME-SERIES CROSS-SECTION DATA
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DOI：
10.2307/2082979
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发表时间：
1995-09-01
影响因子：
6.8
通讯作者：
KATZ, JN
中科院分区：
文献类型：
--
作者：
BECK, N;KATZ, JN
我们研究了时间序列横截面模型估计中的一些问题，对许多已发表的研究的结论提出了质疑，特别是在比较政治经济学领域。我们证明 Parks 的广义最小二乘法会产生标准误差，从而导致极度过度自信，通常会低估变异性 50% 或更多。我们还提供了标准误差的替代估计器，当误差结构显示此类模型中发现的复杂性时，该估计器是正确的。蒙特卡罗分析表明，这些“面板校正标准误差”表现良好。我们的方法的实用性是通过对一种“社会民主社团主义”模型的重新分析来证明的。
We examine some issues in the estimation of time-series cross-section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these ''panel-corrected standard errors'' perform well. The utility of our approach is demonstrated via a reanalysis of one ''social democratic corporatist'' model.