Identification and Estimation in Structural Econometric Models

结构计量经济学模型中的识别和估计

• 批准号: 0833058
• 负责人: Rosa Matzkin
• 金额: $ 18.12万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0833058&HistoricalAwards=false
• 关键词: Identification; Estimation; Structural; Econometric; Models

Structural econometric models are essential tools for many empirical studies in fields such asindustrial organization, labor and public economics, and development economics. The analysisof identification is a first necessary step in any such studies. In many of these models, such asthose involving multidimensional optimization or equilibrium conditions, the variables of interestare determined simultaneously. A recent result by Benkard and Berry (2004) has shown thatidentification results that were used for a long time to determine identification, in simultaneousequations models, are incorrect. Hence, other than in the restrictive linear specification modelswith additive unobservable random terms, studied decades ago, little is known at present aboutthe conditions for identification in structural simultaneous equation models.In this project, the PI will develop correct conditions for identification of systems of simultaneousequations, in parametric and nonparametric models, with additive and nonadditive unobservablevariables. Since a large body of previous work in econometrics has relied on the previous incorrectconditions, the PI will also analyze under what additional conditions in the structural models, thoseprevious results still hold.The identification conditions that will be developed will be used to guide the discovery ofnew methods for estimation in nonparametric simultaneous equations, which will be consistent,asymptotically normal, and easy to compute.Since in many of the structural econometric models encountered in applied fields in economics,one encounters situations where observations on the actual values of endogenous variables, such as profits or utility values, is limited, the identification and estimation results will be extended to such situations, where the endogenous variables is simultaneous equations models are latent.To guide the PI in the development of the new methods, and to facilitate the adoption andunderstanding of the new methods, she will consider applications to several leading models inapplied economics, such as models of consumer demand, discrete choice models, hedonic equilibrium models, Nash equilibrium, and models of survey response errors.Intellectual Merit of the Proposed ActivityThe results about identification of simultaneous equations that the PI will develop will allowapplied econometricians to determine the elements that can be identified in an econometricmodel, given their available data. The results will be applicable to very general models, whichdo not specify parametric structures either for the unknown functions or for the distributions ofthe unobservable random terms, as well as to more restrictive, parametric models. The resultsabout estimation of nonparametric models will allow researchers to estimate such models withoutimposing parametric restrictions.Since structural econometric models where the values of the variables of interest are determinedsimultaneously is widespread in most applied fields in economics, these results are predicted tohave a very wide impact. Moreover, by opening the road to new ways of analyzing identificationand estimation in nonparametric simultaneous equation models, it is expected that a wave of newtheoretical results will follow, as a result of the research in this project.Broader ImpactsModels where several variables of interest are determined simultaneously are widespread in,among others, engineering and the social sciences. The methods that will be developed in thisproject will be suitable for applications in these sciences, and through them, they will benefit societyat large. With this aim, the results of the project will be disseminated widely. By involving agraduate student in this research, it is expected that he/she will apply the new results in his/herdissertation and/or develop new related results.

结构计量经济学模型是产业组织、劳动与公共经济学、发展经济学等领域许多实证研究的重要工具。在任何此类研究中，对身份的分析都是必要的第一步。在许多这样的模型中，例如涉及多维优化或平衡条件的模型，利益变量是同时确定的。Benkard和Berry(2004)最近的一个结果表明，在同时序列模型中，长期用于确定身份的识别结果是不正确的。因此，除了几十年前研究的具有可加不可观测随机项的限制性线性规范模型外，目前对结构联立方程模型的辨识条件知之甚少。在本项目中，PI将为具有可加和非可加不可观测变量的参数和非参数模型中的同时序列系统的辨识提供正确的条件。由于计量经济学以前的大量工作都依赖于先前不正确的条件，PI还将分析结构模型中哪些附加条件，这些先前的结果仍然有效。将开发的识别条件将被用来指导发现非参数联立方程中估计的新方法，这些方法将是一致的，渐近正态的，并且易于计算。由于在经济学应用领域中遇到的许多结构计量经济学模型中，人们遇到对内生变量的实际值的观察有限的情况，例如利润或效用值，识别和估计结果将扩展到这种情况，在内生变量是联立方程模型的地方，联立方程模型是滞后的。为了指导PI开发新方法，并促进新方法的采用和理解，她将考虑应用经济学中几个主要模型的应用，如消费者需求模型、离散选择模型、享乐均衡模型、纳什均衡模型和调查响应误差模型。拟议活动的智力价值PI将开发的关于联立方程识别的结果将使应用经济学家能够确定在计量经济学模型中可以识别的元素，只要他们有可用的数据。所得结果将适用于非常一般的模型，这些模型既不指定未知函数的参数结构，也不指定不可观测随机项的分布，以及更具限制性的参数模型。关于非参数模型估计的结果将使研究人员能够在不设置参数约束的情况下估计这类模型。由于同时确定感兴趣变量的值的结构计量经济学模型在经济学的大多数应用领域中广泛存在，这些结果预计将产生非常广泛的影响。此外，通过开辟在非参数联立方程模型中分析辨识和估计的新方法，预计作为该项目研究的结果，将会有一波新的理论结果。同时确定几个感兴趣的变量的博德影响模型在工程和社会科学中广泛存在。该项目将开发的方法将适用于这些科学的应用，通过它们，它们将造福于整个社会。本着这一目标，将广泛传播该项目的成果。通过让在校学生参与这项研究，期望他/她能在论文中应用新的研究成果和/或发展新的相关研究成果。