This paper proposes a method for estimating multiple change points in panel data models with unobserved individual effects via ordinary least-squares (OLS). Typically, in this setting, the OLS slope estimators are inconsistent due to the unobserved individual effects bias. As a consequence, existing methods remove the individual effects before change point estimation through data transformations such as first-differencing. We prove that under reasonable assumptions, the unobserved individual effects bias has no impact on the consistent estimation of change points. Our simulations show that since our method does not remove any variation in the dataset before change point estimation, it performs better in small samples compared to first-differencing methods. We focus on short panels because they are commonly used in practice, and allow for the unobserved individual effects to vary over time. Our method is illustrated via two applications: the environmental Kuznets curve and the U.S. house price expectations after the financial crisis.

中文翻译：

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具有时变个体效应的面板数据中的变化点估计 *

本文提出了一种通过普通最小二乘法 (OLS) 估计具有未观察到的个体效应的面板数据模型中的多个变化点的方法。通常，在这种情况下，OLS 斜率估计量由于未观察到的个体效应偏差而不一致。因此，现有方法通过数据转换（例如一阶差分）在变化点估计之前去除个体影响。我们证明，在合理的假设下，未观察到的个体效应偏差对变化点的一致估计没有影响。我们的模拟表明，由于我们的方法在变化点估计之前没有消除数据集中的任何变化，因此与一阶差分方法相比，它在小样本中表现更好。我们专注于短面板，因为它们在实践中很常用，并允许未观察到的个体效应随时间变化。我们的方法通过两个应用来说明：环境库兹涅茨曲线和金融危机后的美国房价预期。