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英文文献：Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure-面板数据具有横截面依赖特征的多层次因素结构
英文文献作者：Carlos Vladimir Rodríguez-Caballero
英文文献摘要：
A panel data model with a multi-level cross-sectional dependence is proposed. The factor structure is driven by top-level common factors as well as non-pervasive factors. I propose a simple method to filter out the full factor structure that overcomes limitations in standard procedures which may mix up both levels of unobservable factors and may hamper the identification of the model. The model covers both stationary and non-stationary cases and takes into account other relevant features that make the model well suited to the analysis of many types of time series frequently addressed in macroeconomics and finance. The model makes it possible to examine the time series and cross-sectional dynamics of variables allowing for a rich fractional cointegration analysis. A Monte Carlo simulation is conducted to examine the finite sample features of the suggested procedure. Findings indicate that the methodology proposed works well in a wide variety of data generation processes and has much lower biases than the alternative estimation methods either in the I(0) or I(d) cases.
提出了一种具有多层次横截面依赖关系的面板数据模型。因素结构由顶层共同因素和非普遍因素驱动。我提出了一种简单的方法来过滤掉所有的因素结构，它克服了标准程序的局限性，这些局限性可能混合了两种不可观察的因素，并可能妨碍模型的识别。该模型涵盖了平稳和非平稳情况，并考虑了其他相关特征，这些特征使该模型非常适合分析在宏观经济和金融中经常提到的许多类型的时间序列。该模型使它有可能检查时间序列和横向动态变量允许一个丰富的分数协整分析。蒙特卡罗模拟进行了检查有限样本特征的建议程序。结果表明，所提出的方法在各种各样的数据生成过程中工作良好，并且在I(0)或I(d)情况下比其他估计方法有更低的偏差。
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