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摘要翻译：
本文提出了一种新的面板数据模型和估计方法，使我们能够识别异质结构突变。我们使用分组模式对个体异质性进行建模。对于每一组，我们允许在系数中出现共同的结构突变。但是，这些中断的数量、时间和大小可以在不同的组中不同。我们发展了一个分组固定效应方法和自适应群融合套索的混合估计过程。我们证明了我们的方法能够一致地识别潜在的基团结构，检测结构突变，并估计回归参数。Monte Carlo结果表明，该方法在有限样本中具有良好的性能。对收入和民主之间关系的实证应用说明了考虑异质性结构突变的重要性。
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英文标题：
《Heterogeneous structural breaks in panel data models》
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作者：
Ryo Okui and Wendun Wang
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
This paper develops a new model and estimation procedure for panel data that allows us to identify heterogeneous structural breaks. We model individual heterogeneity using a grouped pattern. For each group, we allow common structural breaks in the coefficients. However, the number, timing, and size of these breaks can differ across groups. We develop a hybrid estimation procedure of the grouped fixed effects approach and adaptive group fused Lasso. We show that our method can consistently identify the latent group structure, detect structural breaks, and estimate the regression parameters. Monte Carlo results demonstrate the good performance of the proposed method in finite samples. An empirical application to the relationship between income and democracy illustrates the importance of considering heterogeneous structural breaks.
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PDF链接：
https://arxiv.org/pdf/1801.04672
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