Workshops

The Applied Statistics Workshop 2017

※統計数理研究所リスク解析戦略研究センター金融・保険リスク研究プログラムとの共催ワークショップ

※ 2017年7月18日現在の予定です。

日時

2017年9月15日(金 Friday) 16:50-18:35

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

Partha Lahiri (University of Maryland)
TBA

要旨(Abstract)  

 

日時

2017年10月6日(金 Friday) 16:50-18:35

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

TBA

要旨(Abstract)  

 

日時

2017年10月20日(金 Friday) 16:50-18:35

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

TBA

要旨(Abstract)  

 

日時

2017年11月17日(金 Friday) 16:50-18:35

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

TBA

要旨(Abstract)  

 

日時

2017年12月1日(金 Friday) 16:50-18:35

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

TBA

要旨(Abstract)  

 

日時

2017年12月8日(金 Friday) 13:00-18:00 ※開催時間と場所に注意

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)2階 小島コンファレンスルーム [地図]
in Kojima Conference Room on the 2nd floor of the Economics Research Annex (Kojima Hall) [Map]

報告

科研費コンファレンス
「経済統計・政府統計の理論と応用からの提言」

要旨(Abstract)  

 

日時

2018年1月19日(金 Friday) 16:50-18:35

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

Monika Jinchen Hu (Vassar College)
TBA

要旨(Abstract)  

 

 

<以下本年度終了分>

日時

2017年4月7日(金 Friday) 16:50-18:35

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

Mingli Chen (University of Warwick)
"Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management" (with A Belloni and V. Chernozhukov)

要旨(Abstract) We propose Quantile Graphical Models (QGMs) to characterize predictive and conditional independence relationships within a set of random variables of interest. This framework is intended to quantify the dependence in non-Gaussian settings which are ubiquitous in many econometric applications. We consider two distinct QGMs. First, Condition Independence QGMs characterize conditional independence at each quantile index revealing the distributional dependence structure. Second, Predictive QGMs characterize the best linear predictor under asymmetric loss functions. Under Gaussianity these notions essentially coincide but non-Gaussian settings lead us to different models as prediction and conditional independence are fundamentally different properties. Combined the models complement the methods based on normal and nonparanormal distributions that study mean predictability and use covariance and precision matrices for conditional independence. We also propose estimators for each QGMs. The estimators are based on high-dimension techniques including (a continuum of) l1-penalized quantile regressions and low biased equations, which allows us to handle the potentially large number of variables. We build upon recent results to obtain valid choice of the penalty parameters and rates of convergence. These results are derived without any assumptions on the separation from zero and are uniformly valid across a wide-range of models. With the additional assumptions that the coefficients are well-separated from zero, we can consistently estimate the graph associated with the dependence structure by hard thresholding the proposed estimators. Further we show how QGM can be used in measuring systemic risk contributions and the impact of downside movement in the market on the dependence structure of assets' return.

 

 
日時

2017年4月28日(金 Friday) 9:00-10:15, 13:00-14:45 (開催時間にご注意ください)

主催:ミクロ実証分析ワークショップ

こちらはGraudate Level Special Lectureとしての開催となります。

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

Songnian Chen (Hong Kong University of Science and Technology)
"Semiparametric Estimation of Transformation Models"

要旨(Abstract)  

 

日時

2017年4月28日(金 Friday) 16:50-18:35

主催:ミクロ実証分析ワークショップ

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

Songnian Chen (Hong Kong University of Science and Technology)
"Estimation of Censored Regression Models with Endogeneity"

要旨(Abstract)  

 

日時

2017年6月21日(水 Wednesday) 11:00-12:30  ※開催時間・場所にご注意ください。

主催:ミクロ実証分析ワークショップ

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)2階 第3セミナー室 [地図]
in Seminar Room 3 on the 2nd floor of the Economics Research Annex (Kojima Hall) [Map]

報告

Eric Gautier(Toulouse School of Economics)
TBA

要旨(Abstract)  

 

日時

2017年6月22日(木 Thursday) 16:50-18:35  ※開催日にご注意下さい。

主催:ミクロ実証分析ワークショップ、 共催:ミクロ経済学ワークショップ

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

Jeremy Fox(Rice University)
"Heterogenous Production Functions, Panel Data, and Productivity Dispersion"

要旨(Abstract)  

 

日時

2017年6月22日(木 Thursday) 10:25-12:10
2017年6月23日(金 Friday) 10:25-12:10

※開催日時・場所にご注意ください。

主催:ミクロ実証分析ワークショップ

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第2セミナー室 [地図]
in Seminar Room 2 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

Yingyao Hu(Rice University)
"The Econometrics of Unobservables: Applications of Measurement Error Models in Empirical Industrial Organization and Labor Economics "[paper]

要旨(Abstract) This paper reviews recent developments in nonparametric identification of measurement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to an observed distribution. The identification and estimation of measurement error models focus on how to obtain the latent distribution and the measurement error distribution from the observed distribution. Such a framework is suitable for many microeconomic models with latent variables, such as models with unobserved heterogeneity or unobserved state variables and panel data models with fixed effects. Recent developments in measurement error models allow very flexible specification of the latent distribution and the measurement error distribution. These developments greatly broaden economic applications of measurement error models. This paper provides an accessible introduction of these technical results to empirical researchers so as to expand applications of measurement error models.

 

日時

2017年6月23日(金 Friday) 16:50-18:35

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

北川透(University College London)
"Who should be Treated? Empirical Welfare Maximization Methods for Treatment Choice" (joint with Aleksey Tetenov)

要旨(Abstract) One of the main objectives of empirical analysis of experiments and quasi-experiments is to inform policy decisions that determine the allocation of treatments to individuals with different observable covariates. We study the properties and implementation of the Empirical Welfare Maximization (EWM) method, which estimates a treatment assignment policy by maximizing the sample analog of average social welfare over a class of candidate treatment policies. The EWM approach is attractive in terms of both statistical performance and practical implementation in realistic settings of policy design. Common features of these settings include: (i) feasible treatment assignment rules are constrained exogenously for ethical, legislative, or political reasons, (ii) a policy maker wants a simple treatment assignment rule based on one or more eligibility scores in order to reduce the dimensionality of individual observable characteristics, and/or (iii) the proportion of individuals who can receive the treatment is a priori limited due to a budget or a capacity constraint. We show that when the propensity score is known, the average social welfare attained by EWM rules converges at least at n -1/2 rate to the maximum obtainable welfare uniformly over a minimally constrained class of data distributions, and this uniform convergence rate is minimax optimal. We examine how the uniform convergence rate depends on the richness of the class of candidate decision rules, the distribution of conditional treatment effects, and the lack of knowledge of the propensity score. We offer easily implementable algorithms for computing the EWM rule and an application using experimental data from the National JTPA Study.

 

日時

2017年7月14日(金 Friday) 16:50-18:35

場所

東京大学大学院経済学研究科 学術交流棟 (小島ホール)1階 第1セミナー室 [地図]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

報告

金谷信 (Aarhus University)
"Identification and Inference for Many-player Binary-choice Games with Spatial Dependence"

要旨(Abstract) In this paper, we consider identification and inference for many-player binary-choice games in the Bayesian-Nash framework. We discuss that, when spatial dependence among players is introduced, the standard asymptotic inference scheme for spatial data may not allow for parameter identification/estimation in a reasonable way in view of Bayesian-Nash modeling. We propose an alternative asymptotic inference scheme, and establish identification of model parameters under the asymptotic/probabilistic framework that is consistent with the proposed inference scheme.