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摘要翻译：
本文利用工具变量(IV)回归中的模型对称性，导出了因果结构参数的不变检验。与一般观点相反，我们证明了当方程误差为异方差和自相关时，存在模型对称性。我们的理论与homoskedastic模型(Andrews，Moreira和Stock(2006)和Chamberlain(2007))的现有结果是一致的。我们利用这些对称性对过辨识模型中的因果关系参数提出了条件积分似然(CIL)检验。理论和数值结果表明，与其他测试相比，CIL测试在功率和实施方面表现良好。我们建议实践者在刚刚识别的模型中使用安德森-鲁宾(AR)测试，在过度识别的模型中使用CIL测试。
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英文标题：
《Optimal Invariant Tests in an Instrumental Variables Regression With
Heteroskedastic and Autocorrelated Errors》
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作者：
Marcelo J. Moreira, Mahrad Sharifvaghefi, Geert Ridder
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最新提交年份：
2021
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分类信息：
一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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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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一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
This paper uses model symmetries in the instrumental variable (IV) regression to derive an invariant test for the causal structural parameter. Contrary to popular belief, we show that there exist model symmetries when equation errors are heteroskedastic and autocorrelated (HAC). Our theory is consistent with existing results for the homoskedastic model (Andrews, Moreira, and Stock (2006) and Chamberlain (2007)). We use these symmetries to propose the conditional integrated likelihood (CIL) test for the causality parameter in the over-identified model. Theoretical and numerical findings show that the CIL test performs well compared to other tests in terms of power and implementation. We recommend that practitioners use the Anderson-Rubin (AR) test in the just-identified model, and the CIL test in the over-identified model.
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PDF链接：
https://arxiv.org/pdf/1705.00231
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