Identification robust Inference in GMM Models Using Stability Restrictions

使用稳定性限制识别 GMM 模型中的稳健推理

• 批准号: 1022623
• 负责人: Sophocles Mavroeidis
• 金额: $ 5.53万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1022623&HistoricalAwards=false
• 关键词: Identification; robust; Inference; GMM; Models

Structural change, typically induced by policy regime shifts, is a common feature of dynamic economic models. The proposed research shows that such changes can be used constructively to improve the identification of structural parameters that are stable over time. This insight is used to develop novel econometric methods that generalize the widely used generalized method of moments (GMM). The proposed methods yield improved inference in models that are used for the analysis of macroeconomic policy, so they have the potential to be widely used in practice.The proposed reseach focuses on time series models that are speci…ed in terms of a set of moment conditions. A leading example is Euler equation models whose parameters are assumed to be stable in the face of policy regime shifts or changes in the volatility of economic shocks. However, the scope of the proposed methods of inference extends beyond this specific example.The contribution of the proposed research is twofold. First, it makes a formal case for using stability restrictions (e.g., immunity to the Lucas critique) as a source of identification of the stable structural parameters in economic models, or put di¤erently, for using structural change to identify stable dynamic causal e¤ects. The key insight is that changes in the distribution of the data induced by, for example, policy regime shifts, provide additional exogenous variation that can be usefully exploited for inference. This information is ignored by the usual GMM approach that relies only on full-sample exclusion or cross-equation restrictions to identify the structural parameters of the model.The second contribution is to develop new econometric methods for structural inference that exploit the information in stability restrictions and require only mild assumptions about the nature of instability in the distribution of the data. Specifically, they do not require any prior knowledge about the incidence, number and timing of breaks. Because no assumptions about identification are required, the main regularity conditions are strictly weaker than those used to justify the stability tests that are widely used in applied work. Therefore, the scope of the proposed methods is very wide. Application of the proposed methods to a widely used new Keynesian macroeconomic model shows that these methods are very useful in practice.Intellectual Merit: This research, which is in collaboration with Leandro Magnusson (Tulane U), is important to advancing knowledge in the field of economics by developing essential new quantitative methods with transformative potential. Broader Impact: (i) The project will provide essential tools for the evaluation of models used for economic policy. (ii) the results will be broadly disseminated to enhance scientific understanding through presentations in conferences, and publication in peer-reviewed academic journals.

结构变化通常是由政策体制转变引起的，是动态经济模型的一个共同特征。所提出的研究表明，这种变化可以建设性地用于改进随时间稳定的结构参数的识别。这一见解被用来开发新的计量经济学方法，推广广泛使用的广义矩法（GMM）。所提出的方法在用于宏观经济政策分析的模型中产生了改进的推理，因此它们具有在实践中广泛应用的潜力。提出的研究重点是特殊的时间序列模型&amp；用一组力矩条件表示。一个典型的例子是欧拉方程模型，它的参数被假设在面对政策制度的转变或经济冲击的波动性变化时是稳定的。然而，所提出的推理方法的范围超出了这个具体的例子。拟议研究的贡献是双重的。首先，它提出了使用稳定性限制（例如，对卢卡斯批判的免疫力）作为经济模型中稳定结构参数识别的来源的正式案例，或者换句话说，使用结构变化来识别稳定的动态因果关系。关键的见解是，由政策制度转变等因素引起的数据分布变化，提供了额外的外生变化，可以有效地利用这些变化进行推理。通常的GMM方法只依赖全样本排除或交叉方程限制来识别模型的结构参数，而忽略了这些信息。第二个贡献是为结构推理开发新的计量经济学方法，利用稳定性限制中的信息，只需要对数据分布中不稳定性的性质进行温和的假设。具体来说，他们不需要事先了解休息的发生率、次数和时间。由于不需要关于识别的假设，因此主要的正则性条件比用于证明在实际工作中广泛使用的稳定性试验的条件要弱得多。因此，提出的方法的范围是非常广泛的。将所提出的方法应用于广泛使用的新凯恩斯宏观经济模型表明，这些方法在实践中非常有用。智力价值：这项研究是与Leandro Magnusson（杜兰大学）合作进行的，通过开发具有变革潜力的重要的新定量方法，对推进经济学领域的知识非常重要。更广泛的影响：(i)该项目将为评价用于经济政策的模型提供基本工具。（ii）通过在会议上的演讲和在同行评审的学术期刊上发表，广泛传播研究结果，以增进科学认识。