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国际经济学workshop：Inference in Conditional Moment Models
发布日期：2017-04-12 11:19 来源：北京大学国家发展研究院
时间：2017年4月12日（周三）10：30－12：00 am
地点：北大国发院/中国经济研究中心万众楼一楼小教室
主持人：余淼杰 余昌华 王歆
主讲人：洪圣杰（Tsinghua University）
主题：Inference in Conditional Moment Models
主讲人将会介绍两篇文章，其中第二篇是第一篇的拓展，这两篇文章的题目和摘要如下。
Inference in Semiparametric Conditional Moment Models with Partial Identification
Abstract: This paper develops inference methods for conditional moment models in which the unknown parameter is possibly partially identified and may contain infinite-dimensional components. For a conjectured restriction on the parameter, we consider testing the hypothesis that the restriction is satisfied by at least one element of the identified set. We propose using the sieve minimum of a Kolmogorov–Smirnov type statistic as the test statistic, derive its asymptotic distribution, and provide consistent bootstrap critical values. In this way a broad family of restrictions can be consistently tested, making the proposed procedure applicable to testing the model specification and constructing confidence set for any given component or some feature of the parameter. Our methods are robust to partial identification, and allow for the moment functions to be nonsmooth. As an illustration, we apply the proposed inference methods to study the quantile instrumental variable Engel curves for gasoline in Brazil. A Monte Carlo study demonstrates finite sample performance.
Inference in Conditional Moment Inequality Models with Unknown Functions
Abstract: In this paper, we consider inference on conditional moment inequalities, in which the unknown parameter may contain infinite-dimensional components. We transfer moment inequalities into equalities by introducing a slackness function as a nuisance parameter. For inference on these equalities, we propose using the sieve minimum of a Kolmogorov-Smirnov type statistic as the test statistic, derive its asymptotic distribution, and provide consistent bootstrap critical values. Our methods are robust to partial identification, and allow for the moment functions to be nonsmooth. We extend Hong’s (2017) analysis on conditional moment equalities in the following two directions: First, by allowing for moment inequality constraints, our inference method has a lot more applications; Second, the consistency of our test holds uniformly over a broad family of data generating processes.
主讲人简介：洪圣杰，清华大学经济管理学院经济系助理教授。2005年获武汉大学经济学学士和数学学士学位，2007年获武汉大学经济学硕士学位，2012年获美国威斯康星大学麦迪逊分校经济学博士学位。研究领域为计量经济学理论、应用计量经济学。论文发表于计量经济学领域顶级期刊Journal of Econometrics，并担任Journal of Econometrics的审稿人。
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国际经济学workshop：Inference in Conditional Moment Models
发布日期：2017-04-12 11:19 来源：北京大学国家发展研究院
时间：2017年4月12日（周三）10：30－12：00 am
地点：北大国发院/中国经济研究中心万众楼一楼小教室
主持人：余淼杰 余昌华 王歆
主讲人：洪圣杰（Tsinghua University）
主题：Inference in Conditional Moment Models
主讲人将会介绍两篇文章，其中第二篇是第一篇的拓展，这两篇文章的题目和摘要如下。
Inference in Semiparametric Conditional Moment Models with Partial Identification
Abstract: This paper develops inference methods for conditional moment models in which the unknown parameter is possibly partially identified and may contain infinite-dimensional components. For a conjectured restriction on the parameter, we consider testing the hypothesis that the restriction is satisfied by at least one element of the identified set. We propose using the sieve minimum of a Kolmogorov–Smirnov type statistic as the test statistic, derive its asymptotic distribution, and provide consistent bootstrap critical values. In this way a broad family of restrictions can be consistently tested, making the proposed procedure applicable to testing the model specification and constructing confidence set for any given component or some feature of the parameter. Our methods are robust to partial identification, and allow for the moment functions to be nonsmooth. As an illustration, we apply the proposed inference methods to study the quantile instrumental variable Engel curves for gasoline in Brazil. A Monte Carlo study demonstrates finite sample performance.
Inference in Conditional Moment Inequality Models with Unknown Functions
Abstract: In this paper, we consider inference on conditional moment inequalities, in which the unknown parameter may contain infinite-dimensional components. We transfer moment inequalities into equalities by introducing a slackness function as a nuisance parameter. For inference on these equalities, we propose using the sieve minimum of a Kolmogorov-Smirnov type statistic as the test statistic, derive its asymptotic distribution, and provide consistent bootstrap critical values. Our methods are robust to partial identification, and allow for the moment functions to be nonsmooth. We extend Hong’s (2017) analysis on conditional moment equalities in the following two directions: First, by allowing for moment inequality constraints, our inference method has a lot more applications; Second, the consistency of our test holds uniformly over a broad family of data generating processes.
主讲人简介：洪圣杰，清华大学经济管理学院经济系助理教授。2005年获武汉大学经济学学士和数学学士学位，2007年获武汉大学经济学硕士学位，2012年获美国威斯康星大学麦迪逊分校经济学博士学位。研究领域为计量经济学理论、应用计量经济学。论文发表于计量经济学领域顶级期刊Journal of Econometrics，并担任Journal of Econometrics的审稿人。
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