## Next Seminar

 Date Jan/27/2022 Time Japan Standard Time (UTC+9h) 10:00 ~ 11:30 Speaker Eiji Kurozumi Hitotsubashi University He received Ph.D. in Economics from Hitotsubashi University in March 2000. After the post doctoral position of Japan Society for Promotion and Science, he started working at Hitotsubashi university in October 2000. His research field is theoretical time series analysis. Prof Kurozumi’s works have been published in leading econometrics journals such as Journal of Econometrics, Econometric Theory, Econometrics Journal, Econometric Review, and Journal of Time Series Analysis. He has also received the JSS Ogawa Award and JSS Research Award from the Japan Statistical Society, and Distinguished Author Award from the Journal of Time Series Analysis. Title On the asymptotic behavior of bubble date estimators Abstract In this study, we extend the three-regime bubble model of Pang, Du, and Chong (2021) to allow the forth regime followed by the unit root process after recovery. We provide the asymptotic and finite sample justification of the consistency of the collapse date estimator in the two-regime AR(1) model. The consistency allows us to split the sample before and after the date of collapse and to consider the estimation of the date of exuberation and date of recovery separately. We have also found that the limiting behavior of the recovery date varies depending on the extent of explosiveness and recovering. Chair Qu Feng Nanyang Technological University Hosts TEDS and Nanyang Technological University Venue Click here to join the Zoom Meeting
 LOGISTICS For each TEDS seminar, the time of presentation is about 1 hour. After the presentation, we have 10 minutes for questions and comments. To learn more from our guest speakers and deepen exchanges between all of us, after the presentation, a session of 20 minutes for free talk is set. You can ask further questions regardless of whether they are related to the presentation, or share some idea with the guest or any of us, or introduce yourself to the others, or talk about anything as you please. You can enter or leave freely during the seminar. We hope everybody could enjoy our seminar. (Participants are expected to behave in a manner respectful of the presenter and other participants. Organizers keep rights to mute and/or expel those who engage in inappropriate conduct.)
 Managing Chair (2021) Qingfeng Liu, Otaru University of Commerce, JPN. Co-chairs Qu Feng, Nanyang Technological University, SGP. Yang Feng, New York University, USA. Masamune Iwasawa, Otaru University of Commerce, JPN. Yingying Li, Hong Kong University of Science and Technology, CHN. Nianqing (Paul) Liu, Shanghai University of Finance & Economics, CHN. Ryo Okui, Seoul National University, KOR. abc Advisor Yoshihiko Nishiyama, Kyoto University, JPN. co-chairs Naoya Sueishi, Kobe University, JPN. Takahiro Terasaka, Otaru University of Commerce, JPN. Yoshimasa Uematsu, Tohoku University, JPN. Wenjie Wang, Nanyang Technological University, SGP. Takahide Yanagi, Kyoto University, JPN. Arihiro Yoshimura, Kyoto Sangyo University, JPN. Xinyu Zhang, Chinese Academy of Sciences, CHN.

## Upcoming Seminars

 Date TBA/TBA/2022 Time Japan Standard Time (UTC+9h) TBA Speaker Xiaohua Yu Georg-August-University Göttingen Title TBA Abstract TBA Venue Click here to join the Zoom Meeting
 Date Feb/18/2022 Time Japan Standard Time (UTC+9h) 18:00 ~ 19:30 Speaker Takashi Yamagata University of York Title TBA Abstract TBA Venue Click here to join the Zoom Meeting Hosts TEDS and Nanyang Technological University
 Date Mar/03/2022 Time Japan Standard Time (UTC+9h) 10:00 ~ Speaker Yuan Liao Rutgers University Title TBA Abstract TBA Venue Click here to join the Zoom Meeting Chair Qu Feng Nanyang Technological University
 Date Mar/TBA/2022 Time Japan Standard Time (UTC+9h) TBA Speaker Katsumi Shimotsu The University of Tokyo Title TBA Abstract TBA Venue Click here to join the Zoom Meeting
 Date TBA/TBA/2022 Time Japan Standard Time (UTC+9h) TBA Speaker Chuan Liu Academia Sinica Title TBA Abstract TBA Venue TBA

## Past Seminars

 Date Jan/18/2022 Time Japan Standard Time (UTC+9h) 18:00 ~ 19:30 Speaker Thomas Kneib Georg-August-University Göttingen Title Multivariate Conditional Transformation Models Abstract Regression models describing the joint distribution of multivariate response variables conditional on covariate information have become an important aspect of contemporary regression analysis. However, a limitation of such models is that they often rely on rather simplistic assumptions, e.g.~a constant dependence structure that is not allowed to vary with the covariates or the restriction to linear dependence between the responses only. We propose a general framework for multivariate conditional transformation models that overcomes such limitations and describes the full joint distribution in a tractable and interpretable yet flexible way. Among the particular merits of the framework are that it can be embedded into likelihood-based inference (including results on asymptotic normality) and allows the dependence structure to vary with the covariates. In addition, the framework scales well beyond bivariate response situations. Venue Click here to join the Zoom Meeting Hosts Jointly hosted by TEDS, Joint Research Program of KIER, Kyoto University and Econometrics Seminar of KIER, Kyoto University.

## 2022 New Year TEDS Speech

• He is a statistician, financial econometrician and data scientist,
• He is Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and Professor of Operations Research and Financial Engineering at the Princeton University where he chaired the department from 2012 to 2015.
• He is the winner of The 2000 COPSS Presidents' Award, Morningside Gold Medal for Applied Mathematics (2007), Guggenheim Fellow (2009), Pao-Lu Hsu Prize (2013) and Guy Medal in Silver (2014). He got elected to Academician from Academia Sinica in 2012.
• abc
 Date&Time JST ☼ 東京 Jan/07/2022 11:00 ~ 12:30 CST ☼ 北京 Jan/07/2022 10:00 ~ 11:30 EST ☽ NYC Jan/06/2022 21:00 ~ 22:30 Speaker Jianqing Fan  Princeton University Title Understanding Deep Q-Learning Abstract TBA Venue Click here to join the Zoom Meeting Chair Yoshihiko Nishiyama Kyoto University Q&A Moderated by Qu Feng Nanyang Technological University Hosts Jointly organized by TEDS, Nanyang Technological University, Kansai Keiryo Keizaigaku Kenkyukai（関西計量経済学研究会）and Otaru University of Commerce. Video Click here to see the video

 Date Dec/16/2021 Time Japan Standard Time (UTC+9h) 14:40 ~ 16:10 Speaker Yasumasa Matsuda  Tohoku University Title Functional regression models for spatio-temporal data Abstract Functional regression is an extension of regression when both dependent and independent variables are function-valued. In this talk, we regard spatial data as square integrable function -valued random variables, and construct a regression model for spatio-temporal data by a bounded linear operator on L2(R2), where a convolution operator will be employed. We propose a frequency domain approach to estimate parameters that can overcome typical difficulties in spatial data analysis, including irregularly spaced observation locations with huge sample sizes, lots of NAs and so on. We clarify the asymptotic regime under which the estimator is consistent and asymptotic normal, as asymptotics for spatial data is not trivial at all unlike time series one. We apply our functional regression to the spatial dataset of NTT Docomo human mobility survey in order to examine COVID-19 pandemic in Japan. As I expect audiences are not necessarily familiar with frequency domain approach esp. for spatial data, I will include a brief introductory summary of spectral analysis in the talk. Venue Zoom Meeting Video Click here to see the video Slide Click here to see the slide
 Date Nov/25/2021 Time Japan Standard Time (UTC+9h) 10:00 AM ~ 11:30 PM Speaker Nianqing (Paul) Liu  Shanghai University of Finance & Economics Title Nonparametric estimation of generalized additive model with flexible additive structure Abstract This paper proposes a nonparametric approach to identify and estimate (with kernel) the generalized additive model with flexible additive structure and discrete variable(s) when the link function is unknown.Our approach allowing flexible additive structure provides the applied researchers with enough freedom to balance dimension reduction and robustness of estimate in their nonparametric application. Motivated by concerns from empirical research, our method also allows significant number of discrete variables in the covariates. We effectively transform our model into a generalized additive model with univariate component functions. Our identification and estimation hence follow a procedure adapted from the case with univariate components. The estimators converge to normal distribution in large sample with a one-dimensional convergence rate for the link function and a $$d_k$$-dimensional convergence rate of the component function $$f_k(⋅)$$ defined on $$R^{d_k}$$. Their finite sample performance is evaluated by a simulation study. Venue Zoom Meeting Video Click here to see the video Slide TBA Chair Xinyu Zhang Chinese Academy of Sciences
 Date Nov/04/2021 Time Japan Standard Time (UTC+9h) 10:00 AM ~ 11:30 AM Speaker Weijie Su   Wharton Statistics Department, University of Pennsylvania. Title A Top-Down Approach Toward Understanding Deep Learning Abstract The remarkable development of deep learning over the past decade relies heavily on sophisticated heuristics and tricks. To better exploit its potential in the coming decade, perhaps a rigorous framework for reasoning deep learning is needed, which however is not easy to build due to the intricate details of modern neural networks. For near-term purposes, a practical alternative is to develop a mathematically tractable surrogate model that yet maintains many characteristics of deep learning models. This talk introduces a model of this kind as a tool toward understanding deep learning. The effectiveness of this model, which we term the Layer-Peeled Model, is evidenced by two use cases. First, we use this model to explain an empirical pattern of deep learning recently discovered by David Donoho and his students. Moreover, this model predicts a hitherto unknown phenomenon that we term Minority Collapse in deep learning training. This is based on joint work with Cong Fang, Hangfeng He, and Qi Long. Venue Zoom Meeting Video Click here to see the video Slide Click here to see the slide Chair Masamune Iwasawa Otaru University of Commerce Hosts Jointly hosted by TEDS, Joint Research Program of KIER, Kyoto University and Econometrics Seminar of KIER, Kyoto University.
 Date Oct/22/2021 Time Japan Standard Time (UTC+9h) 10:00 AM ~ 11:30 AM Speaker Dacheng Xiu  University of Chicago Title Test Assets and Weak Factors Abstract Estimation and testing of factor models in asset pricing requires choosing a set of test assets. The choice of test assets determines how well different factor risk premia can be identified: if only few assets are exposed to a factor, that factor is weak, which makes standard estimation and inference incorrect. In other words, the strength of a factor is not an inherent property of the factor: it is a property of the cross-section used in the analysis. We propose a novel way to select assets from a universe of test assets and estimate the risk premium of a factor of interest, as well as the entire stochastic discount factor, that explicitly accounts for weak factors and test assets with highly correlated risk exposures. We refer to our methodology as supervised principal component analysis (SPCA), because it iterates an asset selection step and a principal-component estimation step. We provide the asymptotic properties of our estimator, and compare its limiting behavior with that of alternative estimators proposed in the recent literature, which rely on PCA, Ridge, Lasso, and Partial Least Squares (PLS). We find that the SPCA is superior in the presence of weak factors, both in theory and in finite samples. We illustrate the use of SPCA by applying it to estimate the risk premia of several tradable and nontradable factors, to evaluate asset managers’ performance, and to de-noise asset pricing factors. Venue Zoom meeting Video Click here to see the video Slide Click here to see the slide Chair Wenjie Wang Nanyang Technological University Hosts TEDS and Nanyang Technological University
 Date Oct/12/2021 Time Japan Standard Time (UTC+9h) 10:30 AM ~ 12:00 PM Speaker Yang Feng  New York University Title TBA Abstract We propose a flexible ensemble classification framework, Random Subspace Ensemble (RaSE), for sparse classification. In the RaSE algorithm, we aggregate many weak learners, where each weak learner is a base classifier trained in a subspace optimally selected from a collection of random subspaces. To conduct subspace selection, we propose a new criterion, ratio information criterion (RIC), based on weighted Kullback-Leibler divergence. The theoretical analysis includes the risk and Monte-Carlo variance of the RaSE classifier, establishing the screening consistency and weak consistency of RIC, and providing an upper bound for the misclassification rate of the RaSE classifier. In addition, we show that in a high-dimensional framework, the number of random subspaces needs to be very large to guarantee that a subspace covering signals is selected. Therefore, we propose an iterative version of the RaSE algorithm and prove that under some specific conditions, a smaller number of generated random subspaces are needed to find a desirable subspace through iteration. An array of simulations under various models and real-data applications demonstrate the effectiveness and robustness of the RaSE classifier and its iterative version in terms of low misclassification rate and accurate feature ranking. The RaSE algorithm is implemented in the R package RaSEn on CRAN. Paper: https://jmlr.org/beta/papers/v22/20-600.html Package: https://cran.r-project.org/web/packages/RaSEn/index.html Venue Zoom Meeting Slide Click here to see the slide Video Click here to see the video Passcode: WVe6f+dg
 Date Oct/04/2021 Time Japan Standard Time (UTC+9h) 10:30 AM ~ 12:00 PM Speaker Xinyu Zhang  Chinese Academy of Sciences Title Model averaging by a jackknife criterion for estimating heterogeneous causal effects Abstract The interest of this article is in capturing the heterogeneous treatment effects measured by the conditional average treatment effect (CATE). A model averaging estimation scheme is proposed with multiple candidate linear regression models. The theoretical properties of our proposal are provided. First, it is shown that our proposal is asymptotically optimal in the sense of achieving the lowest possible squared error. Second, the convergence of the weights determined by our proposal is provided under the case where at least one of the candidate models is correctly specified. Simulation results in comparison with several related existing methods favor our proposed method. The method is applied to a dataset from a labor skills training program. Venue Zoom Meeting
 Date Sep/28/2021 Time Japan Standard Time (UTC+9h) 9:00 AM ~ 10:30 AM Speaker Kengo Kato  Cornell University Title Smooth p-Wasserstein distance: structure, empirical approximation, and applications. Abstract Discrepancy measures between probability distributions, often termed statistical distances, are ubiquitous in probability theory, statistics and machine learning. To combat the curse of dimensionality when estimating these distances from data, recent work has proposed smoothing out local irregularities in the measured distributions via convolution with a Gaussian kernel. Motivated by the scalability of this framework to high dimensions, we investigate the structural and statistical behavior of the Gaussian-smoothed p-Wasserstein distance. In particular, we prove that the smoothed p-Wasserstein distance enjoys a parametric empirical convergence rate of $$n^{−1/2}$$, which contrasts the $$n^{−1/d}$$ rate for unsmoothed Wp when $$d \geq 3$$. Also we discuss limit distribution theory for the smooth 1-Wasserstein distance. Video Click here to see the video Passcode: i5ahWcE+
 Date Sep/10/2021 Time Japan Standard Time (UTC+9h) 10:00 AM ~ 11:30 AM Speaker Ryo Okui   Seoul National University Title “Latent group structure in linear panel data models with endogenous regressor” By Junho CHOI and Ryo OKUI Abstract TBA Venue Zoom Meeting Hosts TEDS and Nanyang Technological University

 Transdisciplinary Econometrics & Data Science Seminar - TEDS TEDS gratefully acknowledges the supports of the Japan Society for the Promotion of Science through KAKENHI Grant No. JP19K01582 (Qingfeng Liu), Nanyang Technological University, Joint Research Program of KIER, Kyoto University and Chinese Academy of Sciences.

 E-mail: tedsseminars@gmail.com