Workshops

The Applied Statistics Workshop 2018

※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

※ 2018年7月2日現在の予定です。

 

 

日時

2018年7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

大屋幸輔 (大阪大学)

Frequency-wise causality analysis in infinite order vector autoregressive processes (joint with Ryo Kinoshita and Mototsugu Shintani )

要旨(Abstract) This paper derives the asymptotic properties of frequency-domain causality measure estimator using the vector autoregressive model of infinite order and proposes a test of non-causality at a particular frequency, analogues to the one proposed in previous study. Further the confidence intervals of causality measure and testing procedures to detect possible structural breaks in causality measure at some frequencies are provided using our asymptotic results. Simulation results confirm that our procedure works well with sample size typically available in practice. We illustrate the usefulness of our method via an application to financial data.

 

 

<以下本年度終了分>

日時

2018年5月11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

Enrique Sentana (Center for Monetary and Financial Studies)

"Specification tests for non-Gaussian maximum likelihood estimators"

要旨(Abstract)  We propose generalised DWH specification tests which simultaneously compare three or more likelihood-based estimators of conditional mean and variance parameters in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for GARCH models and in many empirically relevant macro and finance applications involving VARs and multivariate regressions. To design powerful and reliable tests, we determine the rank deficiencies of the differences between the estimators' asymptotic covariance matrices under the null of correct specification and take into account that some parameters remain consistently estimated under the alternative of distributional misspecification. Finally, we provide finite sample results through Monte Carlo simulations.

 

日時

2018年5月25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

堤盛人 (筑波大学)

「組成データ解析の社会経済データへの応用とその可能性」

要旨(Abstract)  割合などのように、値が非負で和が一定となるようなデータは「組成データ」と呼ばれている。その名称自体 は一般的には知られていないものの、至る所で目にするデータの種類である。統計学的には、疑似相関の問題か ら、組成データの分析の際には値の総和が一定であるという定数和制約を考慮する必要があり、地質学を中核に これを考慮した「組成データ解析(Compositional Data Analysis:CoDA)」が発展している(Aitchison, 1986.)。 しかしながら、Aitchison (1986)から既に30年も経過しているにもかかわらず、未だCoDAの研究において取り 扱われているのは自然科学データが大半で、社会経済データを用いた実証研究は皆無に近く、社会科学の分野で はその重要性・有用性がほとんど認識されていない。 本報告では、人口や交通、土地利用などの社会経済データを用いたCoDAの結果を紹介しながら、空間計量経済 学とCoDAの融合など、社会経済データへの応用を主眼にCoDAの新たな展開の可能性を探る。 本報告は、吉田崇紘氏(国立環境研究所・特別研究員)との共同研究によるものである。

 

日時

2018年6月11日(月 Monday) 10:30-12:00 ※日時に注意

※共催: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

Marc Henry (The Pennsylvania State University)

"Sharp bounds and testability of a Roy model of STEM major choices" (joint with Ismael Mourifie and Romuald Meango)

要旨(Abstract) We analyze the empirical content of the Roy model, stripped down to its essential features, namely sector specific unobserved heterogeneity and self-selection on the basis of potential outcomes. We characterize sharp bounds on the joint distribution of potential outcomes and testable implications of the Roy self-selection model under an instrumental constraint on the joint distribution of potential outcomes we call stochastically monotone instrumental variable (SMIV). We show that testing the Roy model selection is equivalent to testing stochastic monotonicity of observed outcomes relative to the instrument. Special emphasis is put on the case of binary outcomes, which has received little attention in the literature to date. For richer sets of outcomes, we emphasize the distinction between pointwise sharp bounds and functional sharp bounds, and its importance, when constructing sharp bounds on functional features, such as inequality measures. We analyze a Roy model of college major choice in Canada and Germany within this framework, and we take a new look at the under-representation of women in STEM.

 

日時

2018年6月22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

マクリン謙一郎 (The University of Chicago)

"Large-Scale Dynamic Predictive Regressions"

要旨(Abstract) We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under this framework, clusters of predictors generate different latent states in the form of predictive densities that are later synthesized within an implied time-varying latent factor model. As a result, the latent inter-dependencies across predictive densities and biases are sequentially learned and corrected. Unlike sparse modeling and variable selection procedures, we do not assume a priori that there is a given subset of active predictors, which characterize the predictive density of a quantity of interest. We test our procedure by investigating the predictive content of a large set of financial ratios and macroeconomic variables on both the equity premium across different industries and the inflation rate in the U.S., two contexts of topical interest in finance and macroeconomics. We find that our predictive synthesis framework generates both statistically and economically significant out-of-sample benefits while maintaining interpretability of the forecasting variables. In addition, the main empirical results highlight that our proposed framework outperforms both LASSO-type shrinkage regressions, factor based dimension reduction, sequential variable selection, and equal-weighted linear pooling methodologies.