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Time-series–cross-section Data

机译：时间序列横截面数据

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This article treats the analysis of ‘time-series–cross-section’ (TSCS) data. Such data consists of repeated observations on a series of fixed units. Examples of such data are annual observations on the political economy of OECD nations in the post-war era. TSCS data is distinguished from ‘panel’ data, in that asymptotics are in the number of repeated observations, not the number of units.The article begins by treating the complications of TSCS data in an ‘old-fashioned’ manner, that is, as a nuisance which causes estimation difficulties. It claims that TSCS data should be analyzed via ordinary least squares with ‘panel correct standard errors’ rather than generalized least squares methods. Dynamics should be modeled via a lagged dependent variable or, if appropriate, a single equation error correction model.The article then treats more modern issues, in particular, the modeling of spatial effects and heterogeneity. It also claims that heterogeneity should be assessed with ‘panel cross-validation’ as well as more standard tests. The article concludes with a discussion of estimation in the presence of a binary dependent variable.
机译：本文将分析“时间序列-横截面”（TSCS）数据。这些数据包括对一系列固定单位的重复观察。此类数据的例子是战后时代对经合组织国家政治经济的年度观察。 TSCS数据与“面板”数据的区别在于，渐进性在于重复观察的次数，而不是单位数。本文从以“老式”方式处理TSCS数据的复杂性开始，即造成估计困难的麻烦。它声称应该使用“面板正确的标准误差”通过普通的最小二乘法而不是广义的最小二乘法来分析TSCS数据。动力学应该通过滞后因变量或（如果适用）单方程误差校正模型来建模。然后，本文将讨论更现代的问题，特别是空间效应和异质性的建模。它还声称应该通过“面板交叉验证”以及更多标准测试来评估异质性。本文最后讨论了存在二进制因变量的情况下的估计。

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