This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement; more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is not large. The proposed approach builds upon existing GMM methodology and develops Distance-type and LM-type tests for detecting a structural break, both when the breakpoint is known as well as when it is unknown. The proposed methodology permits weak exogeneity and/or endogeneity of the regressors. In a simulation study, the method performed well, both in terms of size and power, as well as in terms of successfully locating the time of the structural break. The method is illustrated by testing the so-called ‘Gibrat’s Law’, using a dataset from 4,128 financial institutions, each one observed for the period 2002-2014.

中文翻译：

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具有共同因素的面板的新结构断裂试验

当T固定时，本文针对具有共同因素的面板数据模型的结构破坏开发了新的测试，其中T表示随时间变化的观察次数。对于此类模型，仅在T为“大”的假设下，针对结构破坏的可用测试才有效。但是，这可能是一个严格的要求。更常见的是在具有每年时间频率的数据集中，在这种情况下，即使T不大，样本也可能覆盖相对较长的时间。所提出的方法建立在现有GMM方法论的基础上，并开发了距离类型和LM类型的测试来检测结构性断裂，无论该断裂点是已知的还是未知的。所提出的方法允许回归因子的弱外生性和/或内生性。在仿真研究中，该方法在尺寸和功率方面均表现出色，以及成功定位结构性断裂的时间。通过使用来自4,128个金融机构的数据集（所谓的“吉卜拉特定律”）进行测试来说明该方法，每个金融机构均在2002-2014年期间进行了观察。