Workshops

The Applied Statistics Workshop 2019

※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

※ 2019年6月10日現在の予定です。

日時

2019年7月5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

國濱剛(関西学院大学)

TBA

要旨(Abstract)  

日時

2019年7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

城田慎一郎(理化学研究所)

TBA

要旨(Abstract)  

<以下本年度終了分>

 

日時

2019年4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

福元健太郎(学習院大学)

"Non-Ignorable Attrition in Pairwise Randomized Experiments"

要旨(Abstract)  The pairwise randomized experiments enable robust and efficient causal inference. What if outcomes of some units are missing? One way is to delete missing units and calculate difference-in-means (unitwise deletion estimator, UDE). Another method is to delete the other unit in the pair as well (pairwise deletion estimator, PDE). UDE is biased. Some scholars argue that PDE is unbiased, while opponents criticize that PDE is also biased if attrition is non-ignorable and PDE is less efficient than UDE. By using the potential outcome framework, this study formally shows that PDE can be biased but more efficient than UDE; the pairwise variance estimator of PDE is unbiased in the super-population. I argue that it is easier to interpret PDE as a causal effect than UDE. I also propose a new variance estimator. Finally, in order to show how PDE and UDE work, an application is demonstrated. This paper recommends PDE.

日時

2019年4月26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

Mike So (Hong Kong University of Science and Technology)

"Efficient Estimation of High-Dimensional Dynamic Covariance by Risk Factor Mapping: Applications for Financial Risk Management"

要旨(Abstract)  This paper aims to explore a modified method of high-dimensional dynamic variance-covariance matrix estimation via risk factor mapping, which can yield a dependence estimation of asset returns within a large portfolio with high computational efficiency. The essence of our methodology is to express the time-varying dependence of high-dimensional return variables using the co-movement concept of returns with respect to risk factors. A novelty of the proposed methodology is to allow mapping matrices, which govern the co-movement of returns, to be time-varying. We also consider the exible modeling of risk factors by a copula multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model. Through the proposed risk factor mapping model, the number of parameters and the time complexity are functions of a small number of risk factors instead of the number of stocks in the portfolio, making our proposed methodology highly scalable. We adopt Bayesian methods to estimate unknown parameters and various risk measures in the proposed model. The proposed risk mapping method and financial applications are demonstrated by an empirical study of the Hong Kong stock market. The assessment of the effectiveness of the mapping via risk measure estimation is also discussed.

日時

2019年5月24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

星野崇宏(慶応大学)

「潜在的結果変数の同時分布と因果効果の異質性の識別および推定について」

要旨(Abstract) ルービンの因果効果およびその異質性を推定することは、政策評価や最適割当な ど様々な分野で重要な課題である。但し潜在的結果変数の差のquantileなど、一 般に重要な量を推定するためには潜在的結果変数の同時分布を識別することが必 要となる。しかし無作為化実験であってもこの同時分布を識別することはできな い。 本研究では同時分布を識別できる条件を示す。さらにこれがノンコンプライアン スのある無作為化実験や外部情報が利用できる状況などで成立することを示し、 その推定方法を提案する。シミュレーションデータを用いて推定の精度を議論し、 職業訓練プログラムデータに応用した解析例を示す。 本研究は高畑圭祐氏(慶應義塾大学大学院)との共同研究である。 

日時

2019年6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

北村祐一 (Yale University)

"Methods for nonparametric counterfactual analysis with (or without) convex structure"

要旨(Abstract)  Abstract: Providing counterfactuals given an economic model, while avoiding ad hoc assumptions is an important task. In this talk I argue that there are many models that satisfy certain convexity properties, which, in turn, enable us to calculate counterfactuals and carry out inference about them under minimal assumptions. This typically yield predictions in terms of bounds, and we need inferential procedures that address non-standard features associated with them, but at the same time practically implementable. These tasks typically require optimization in very high dimensions, but they can be carried out under the convexity properties, combined with appropriate algorithms and recent advances in computational resources. Various examples will be discussed. This talk will be based on joint projects with Rahul Deb, Lucas de Lima, Myrto Kalouptsidi, Eduardo Souza Rodrigues, Joerg Stoye and John Quah.