※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1月24日現在の予定です。
＜以下本年度終了分＞
| 日時 | 2017年4月7日（金 Friday） 16:50-18:35 |
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| 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] |
報告 | Mingli Chen (University of Warwick) |
| 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） こちらはGraudate Level Special Lectureとしての開催となります。 |
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| 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] |
報告 | Songnian Chen （Hong Kong University of Science and Technology） |
| 要旨(Abstract) |
| 日時 | 2017年4月28日（金 Friday） 16:50-18:35 |
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| 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] |
報告 | Songnian Chen （Hong Kong University of Science and Technology） |
| 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。 |
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| 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] |
報告 | Eric Gautier（Toulouse School of Economics) |
| 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 |
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| 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] |
報告 | Jeremy Fox（Rice University) |
| 要旨(Abstract) |
| 日時 | 2017年6月22日（木 Thursday） 10:25-12:10 ※開催日時・場所にご注意ください。 |
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| 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] |
報告 | Yingyao Hu（Rice University) |
| 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 |
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| 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] |
報告 | 北川透（University College London) |
| 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 |
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| 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] |
報告 | 金谷信 (Aarhus University) |
| 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. |
| 日時 | 2017年9月15日（金 Friday） 16:50-18:35 |
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| 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] |
報告 | Partha Lahiri (Joint Program in Survay Methodology (JPSM) and University of Maryland) |
| 要旨(Abstract) | There is a growing demand from various federal and local government
agencies to produce high quality estimates on a wide range of
characteristics for a target population and its different small
subgroups. These estimates are often produced using by an empirical best
prediction method that uses an appropriate mixed model in combining
survey data with various related administrative and census data. This
statistical approach has the potential to reduce cost in producing small
area statistics as it takes advantage of the existing databases instead
of requiring new costly survey data collection. The small area estimates
are useful in assessing the well-being of a nation in terms of various
socio-economic and health issues and taking remedial measures to improve
on the situation, if necessary. A second-order unbiased mean square prediction (MSPE) error estimation has been one of the challenging problems in small area estimation for over thirty years. Various approaches have been proposed in the literature. In this talk, I will first review the jackknife method in small area estimation, including the recently proposed Monte-Carlo jackknife method (McJack) in incorporating additional uncertainty due to model selection. This talk is based on my long time research collaborations with Shijie Chen, Jiming Jiang, Thuan Nguyen and Shum-Mei Wan. |
| 日時 | 2017年10月6日（金 Friday） 16:50-18:35 |
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| 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] |
報告 | 村上大輔 （統計数理研究所） |
| 要旨(Abstract) | This study develops a spatially varying coefficient (SVC) model by extending a spatial mixed effects approach called random effects eigenvector spatial filtering. The developed model has the following properties: the SVCs are interpretable in terms of the Moran coefficient, which is a diagnostic statistics for spatial dependence; unlike conventional approaches, the developed approach allows for estimating scales of each SVC; it yields an approximation of a Bayesian SVC model; and, it is computationally efficient and applicable for large samples. Results of a Monte Carlo simulation reveals that our model outperforms conventional approaches, including the geographically weighted regression (GWR) and the eigenvector spatial filtering approaches, in terms of the accuracy of the SVC estimates and computational time. We empirically apply our model to a land price analysis of flood hazards in Japan. |
| 日時 | 2017年10月20日（金 Friday） 16:50-18:35 |
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| 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] |
報告 | 廣瀬雅代 （統計数理研究所） |
| 要旨(Abstract) | In small area estimation, the second-order empirical Bayes confidence interval, the coverage error of which is of the third-order for a large number of areas, is widely used when the sample size within each area is not large enough to make reliable direct estimates based on the design-based approach. Yoshimori and Lahiri (2014) proposed a new type of confidence interval, called the second-order efficient empirical Bayes confidence interval, with a length less than that of the direct confidence interval based on the design-based approach. However, this interval still has two issues caused by their area-specific adjustment factor. In this talk, we will introduce more tractable confidence interval through several new non-area-specific adjusted maximum likelihood method in order to prevent such two issues, which proposed in Hirose (2017). Moreover, we will also show the results of our simulation study and real data analysis for showing overall superiority of our confidence interval method over the other methods. |
| 日時 | 2017年10月27日（金 Friday） 16:50-18:35 |
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| 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] |
報告 | Ulrich Mueller (Princeton University) |
| 要旨(Abstract) | Consider inference about the mean of a population with finite variance, based on an i.i.d. sample. The usual t-statistic yields correct inference in large samples, but heavy tails induce poor small sample behavior. This paper combines extreme value theory for the smallest and largest observations with a normal approximation for the t-statistic of a truncated sample to obtain more accurate inference. This alternative approximation is shown to provide a refinement over the standard normal approximation to the full sample t-statistic under more than two but less than three moments, while the bootstrap does not. Small sample simulations suggest substantial size improvements over the bootstrap. |
| 日時 | 2017年12月8日（金 Friday） 13:00-18:00 ※開催時間と場所に注意 |
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| 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] |
報告 | 科研費コンファレンス |
| 要旨(Abstract) |
| 日時 | 2018年1月19日（金 Friday） 16:50-18:35 |
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| 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] |
報告 | Monika Jingchen Hu (Vassar College) |
| 要旨(Abstract) | We present a Bayesian model for estimating the joint distribution of multivariate categorical data when units are nested within groups. Such data arise frequently in social science settings, for example, people living in households. The model assumes that (i) each group is a member of a group-level latent class, and (ii) each unit is a member of a unit-level latent class nested within its group-level latent class. This structure allows the model to capture dependence among units in the same group. It also facilitates simultaneous modeling of variables at both group and unit levels. We develop a version of the model that assigns zero probability to groups and units with physically impossible combinations of variables. We apply the model to estimate multivariate relationships in a subset of the American Community Survey. Using the estimated model, we generate synthetic household data that could be disseminated as redacted public use files with high analytic validity and low disclosure risks. |