摘要翻译：
本文考虑了由未观测因素引起的截面相关的大n和大T非均质板的单位根检验问题。我们重新考虑了文献中流行的两种方法，即Moon和Perron(2004)的方法和Bai和Ng(2004)提出的恐慌设置。虽然这两个框架被认为是完全不同的设置，但我们证明，在高斯新息的情况下，这两个框架是渐近等价的，因为这两个实验都是具有相同中心序列的局部渐近正态(LAN)。利用Le Cam的统计实验理论，我们确定了局部渐近功率包络，并在两种情况下联合导出了一个最优检验。我们发现，流行的Moon and Perron(2004)和Bai and Ng(2010)检验只有在异质成分的长期方差不存在异质性的情况下才达到幂包络。新的检验是渐近一致最强大的，而不考虑可能的异质性。此外，对于任何一个满足轻度正则性条件的测试，在两种数据生成过程中，其规模和局部渐近功率都是相同的。因此，应用研究人员不需要决定两个框架中的一个来进行单位根测试。Monte-Carlo模拟证实了我们的渐近结果，并证明了当各截面单元之间的异质激波的方差显著不同时，有限样本功率的显著提高。
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英文标题：
《Local Asymptotic Equivalence of the Bai and Ng (2004) and Moon and
  Perron (2004) Frameworks for Panel Unit Root Testing》
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作者：
Oliver Wichert, I. Gaia Becheri, Feike C. Drost, Ramon van den Akker
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最新提交年份：
2019
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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 considers unit-root tests in large n and large T heterogeneous panels with cross-sectional dependence generated by unobserved factors. We reconsider the two prevalent approaches in the literature, that of Moon and Perron (2004) and the PANIC setup proposed in Bai and Ng (2004). While these have been considered as completely different setups, we show that, in case of Gaussian innovations, the frameworks are asymptotically equivalent in the sense that both experiments are locally asymptotically normal (LAN) with the same central sequence. Using Le Cam's theory of statistical experiments we determine the local asymptotic power envelope and derive an optimal test jointly in both setups. We show that the popular Moon and Perron (2004) and Bai and Ng (2010) tests only attain the power envelope in case there is no heterogeneity in the long-run variance of the idiosyncratic components. The new test is asymptotically uniformly most powerful irrespective of possible heterogeneity. Moreover, it turns out that for any test, satisfying a mild regularity condition, the size and local asymptotic power are the same under both data generating processes. Thus, applied researchers do not need to decide on one of the two frameworks to conduct unit root tests. Monte-Carlo simulations corroborate our asymptotic results and document significant gains in finite-sample power if the variances of the idiosyncratic shocks differ substantially among the cross sectional units.
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PDF链接：
https://arxiv.org/pdf/1905.11184