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9月21日現在の予定です。

日時

2018年9月28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

Sanjay Chaudhuri (National University of Singapore)

An easy-to-use empirical likelihood ABC method

要旨(Abstract) Many scientifically well-motivated statistical models in natural, engineering and environmental sciences are specified through a generative process, but in most cases it may not be possible to write down a likelihood for these models analytically. Approximate Bayesian computation (ABC) methods, which allow Bayesian inference in these situations, are typically computationally intensive. Recently, empirical
likelihood based ABC methods, which are computationally attractive, have been suggested in the literature. The current empirical likelihood methods rely on the availability of a set of suitable analytically tractable estimating equations. We propose an easy-to-use empirical likelihood ABC method, where the only inputs required are a choice of summary statistic, it’s observed value, and the ability to simulate summary statistics for any parameter value under the model. It is shown that the posterior obtained using the proposed method is consistent, and its performance is explored using various examples.

Joint work with Shubhroshekhar Ghosh, David Nott and Pham Kim Cuc, National University of Singapore.

 

日時

2018年10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

江口真透(統計数理研究所)

一般化混合モデルの提案とその統計的性質

要旨(Abstract) 混合モデルにおいて対数尤度関数は凸性を失うが柔軟なモデリングを可能にする.この発表では,情報幾何の発想から密度関数,予測関数,ハザード関数,ロス関数などの空間上の測地線がコルモゴロフ・南雲(KN)平均によって構築されることを紹介する.この測地線はアルキメディアン・コピュラとも密接な関係がある.特にロス関数のKN平均による結合は一般化混合モデルと呼ばれる統計モデルが誘導される.この統計的な性質を考察し,従来の混合モデルと対数尤度関数の関係を比較検討する. 

 

日時

2018年10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

持橋大地(統計数理研究所)

TBA

要旨(Abstract)  

 

日時

2018年11月2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

栗木哲(統計数理研究所)

TBA

要旨(Abstract)  

 

日時

2018年12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

鈴木大慈 (東京大学)

TBA

要旨(Abstract)  

 

日時

2018年12月21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

井上篤(Vanderbilt University)

TBA

要旨(Abstract)  

 

<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.

 

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.

 

 
日時

2018年9月18日(火 Tuesday) 16:30-17:45 ※日時に注意

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)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 West (Duke University)

"Bayesian Forecasting of High-Dimensional Count-Valued Time Series"

要旨(Abstract)  Problems of forecasting many, related time series of counts arise in many areas, and are particularly prevalent in consumer demand and sales contexts. With a focus on improving multi-step ahead forecasting of daily sales of many supermarket items across a system of outlets, we have developed new classes of models to address efficiency, efficacy and scalability of dynamic models based on the concept of decouple/recouple applied to multiple series that are individually represented via novel univariate state-space models. The latter involve dynamic generalized linear models for binary and conditionally Poisson time series, with dynamic random effects for over-dispersion, allowing use of dynamic covariates in both binary and non-zero count components. Sequential Bayesian analysis allows fast, parallel analysis of sets of decoupled time series. New multivariate models then enable information sharing in contexts when data at a more highly aggregated level provide more incisive inferences on shared patterns such as trends and seasonality. This involves a novel multi-scale approach? one example of the concept of decouple/recouple in time series? that avoids the complexity and computational challenges of traditional hierarchical modelling approaches. The analysis incorporates cross-series linkages while insulating parallel estimation of univariate models, hence enables scalability in the number of series.
 Extension of these models are dynamic count mixture models to apply to forecasting individual customer transactions, coupled with a novel probabilistic model for predicting counts of items per transaction. The latter involves a new dynamic binary cascade concept that contributes two main features: first, it aids in resolving some of the otherwise unpredictable variation in the sales series; second, it allows probabilistic inference on rare events (otherwise unpredictable and very infrequent “high” outcomes on individual series). The resulting transactions-sales models allow use of dynamic covariates in both transaction and sales levels components, and can incorporate a diverse range of trend, seasonal, price, promotion, random effects and other outlet-specific predictors at the level of individual items. Again, sequential Bayesian analysis allows fast, parallel analysis of sets of decoupled time series, while being automatically adaptable across items that may exhibit widely varying characteristics. Further, the multi-scale approach shares information across series at the transaction level.
 The motivating case study context of many-item, multi-period, multi-step ahead supermarket sales forecasting provides examples that demonstrate improved forecast accuracy in a range of traditional and statistical metrics, while also illustrating the benefits of full probabilistic models. The talk will discuss a range of examples and highlight questions of forecast accuracy metrics and broader questions of probabilistic forecast accuracy assessment, decision analytic choice of point forecasts (when desired) and forecast accuracy comparison.