面向模型隐含工具变量两阶段最小二乘估计器的贝叶斯模型平均最优工具选择
Optimal Instrument Selection using Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators
AI总结：
针对MIIV-2SLS估计器的工具变量误设检验缺乏特异性及弱工具问题，提出MIIV-2SBMA估计器，模拟显示其参数估计性能与原方法相当，且工具特异性过度识别检验检测功效更高，并通过实证示例验证了应用价值。
摘要：
模型隐含工具变量两阶段最小二乘（MIIV-2SLS）是一种用于潜变量模型的有限信息、逐方程非迭代估计器，配套有针对特定方程的模型误设检验。该类检验的一个问题是缺乏特异性，仅能表明部分工具变量存在问题，但无法指出具体是哪些；第二个潜在问题是弱工具变量，即对目标变量预测能力较差的工具。我们提出一种新型扩展方法，可检测特定工具变量的误设与弱工具问题，将其命名为模型隐含工具变量两阶段贝叶斯模型平均（MIIV-2SBMA）估计器。我们通过模拟研究对比了MIIV-2SBMA与MIIV-2SLS的性能，发现二者在参数估计方面表现相当。此外，在MIIV-2SBMA框架下开发的特定工具变量过度识别检验，检测问题工具与弱工具的功效有所提升。最后，我们通过一个实证示例演示了MIIV-2SBMA的应用。
英文摘要：
Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, non-iterative estimator for latent variable models. Associated with this estimator are equation specific tests of model misspecification. One issue with equation specific tests is that they lack specificity, in that they indicate that some instruments are problematic without revealing which specific ones. Instruments that are poor predictors of their target variables (weak instruments) is a second potential problem. We propose a novel extension to detect instrument specific tests of misspecification and weak instruments. We term this the Model-Implied Instrumental Variable Two-Stage Bayesian Model Averaging (MIIV-2SBMA) estimator. We evaluate the performance of MIIV-2SBMA against MIIV-2SLS in a simulation study and show that it has comparable performance in terms of parameter estimation. Additionally, our instrument specific overidentification tests developed within the MIIV-2SBMA framework show increased power to detect specific problematic and weak instruments. Finally, we demonstrate MIIV-2SBMA using an empirical example.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
