Next Seminar

Speaker Bruce E. Hansen University of Wisconsin-Madison
Date August/26/2022
Time Japan Standard Time (UTC+9h)
10:00 ~ 11:30
Venue Click here to join the Zoom Meeting Meeting ID: 860 3472 0469 Passcode: 235857
Chair Naoya Sueishi Kobe University
Hosts TEDS, JSPS KAKENHI (B) No. 22H00833 (Liu).
  • A Modern Gauss-Markov Theorem, Econometrica, (2022), 90, 1283-1294.
  • Inference for Iterated GMM Under Misspecification, with Seojeong Lee, Econometrica, (2021), 89, 1419-14447.
  • Least Squares Model Averaging, Econometrica, (2007), 75, 1175-1189.
  • abc Image
    Title Standard Errors for Two-Way Clustering with Serially Correlated Time Effects
    Abstract We propose improved standard errors and an asymptotic distribution theory for two-way clustered panels. Our proposed estimator and theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods, including the popular two-way cluster standard errors of Cameron, Gelbach, and Miller (2011) and the cluster bootstrap of Menzel (2021). Our asymptotic distribution theory is the first which allows for this level of inter-dependence among the observations. Under weak regularity conditions, we demonstrate that the least squares estimator is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically standard normal, permitting conventional inference. We present simulation evidence that confidence intervals constructed with our proposed standard errors obtain superior coverage performance relative to existing methods. We illustrate the relevance of the proposed method in an empirical application to a standard Fama-French three-factor regression.

    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 several 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 (2022)

    Qu Feng, Nanyang Technological University, SGP.
    Wenjie Wang, Nanyang Technological University, SGP.
    Former Managing Chair


    Liyu Dou, The Chinese University of Hong Kong (Shenzhen), CHN.
    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.
    Qingfeng Liu, Hosei University, JPN.
    abc Global


    Yoshihiko Nishiyama, Kyoto University, JPN.


    Ryo Okui, The University of Tokyo, JPN.
    Naoya Sueishi, Kobe University, JPN.
    Wei Sun, Jilin University, CHN.
    Takahiro Terasaka, Otaru University of Commerce, JPN.
    Yoshimasa Uematsu, Hitotsubashi University, JPN.
    Takahide Yanagi, Kyoto University, JPN.
    Arihiro Yoshimura, Kyoto Sangyo University, JPN.
    Xinyu Zhang, Chinese Academy of Sciences, CHN.

    Upcoming Seminars

    Prof. Martin Weidner Date September/29/2022
    Time Japan Standard Time (UTC+9h)
    Speaker Martin Weidner University of Oxford
    Title TBA.
    Abstract TBA.
    Venue Click here to join the Zoom Meeting Meeting ID: TBA. Passcode: TBA.
    Chair Ryo Okui The University of Tokyo
    Hosts TEDS, JSPS KAKENHI (B) No. 22H00833 (Liu).

    Past Seminars

    Prof. Ostu Date July/08/2022
    Time Japan Standard Time (UTC+9h)
    Speaker Taisuke OtsuThe London School of Economics and Political Sciences
    Title Likelihood Inference Under Alternative Asymptotics
    Abstract The seminar will be based on the following three papers: (i) Empirical Likelihood for Network Data (with Yukitoshi Matsushita); (ii) Multiway Empirical Likelihood (with Harold Chiang and Yukitoshi Matsushita); (iii) Jackknife Empirical Likelihood: Small Bandwidths, Sparse Network and High-Dimensional Asymptotics (with Yukitoshi Matsushita).
    Venue Click here to join the Zoom Meeting Meeting ID: 313 176 9518 Passcode: 119549
    Chair Qihui Chen The Chinese University of Hong Kong, Shenzhen
    Hosts TEDS and The Chinese University of Hong Kong, Shenzhen

    Prof. shi zhentao Date May/27/2022
    Time Japan Standard Time (UTC+9h)
    9:30 ~ 11:00
    Speaker Zhentao Shi Georgia Institute of Technology
    Title Boosted Hodrick-Prescott Filter Is More General Than You Think
    Abstract Recent historical economic events such as the Great Recession and the Covid-19 recession have reignited the debate on econometric methodology for filtering low-frequency macroeconomic time series. While the Hodrick-Prescott (HP) filter remains, arguably, the off-the-shelf choice, the boosted HP (bHP) filter emerges as its modern machine learning upgrade for the data-rich environment. This paper sheds light on understanding the versatility of bHP in fitting time series of various nonstationary patterns. We establish a new asymptotic result that each iteration of the HP filter shrinks complex exponential functions proportionally toward zero. This founding opens a unified approach to tackle trends generated by unit root processes, higher order integrated processes, and local-to-unity processes. In an asymptotic framework where the penalty tuning parameter expands with the sample size, this theoretical property explains the smoothing effect of the original HP filter, and more importantly, it guarantees that the bHP filter consistently restores all the underlying trending processes mentioned above, in conjunction with potential deterministic trends and structural breaks. When it is applied to the universe of the FRED-QD dataset, bHP timely captures the downturns at crises and recessions and the recoveries that followed. Buttressed by its sound theoretical coverage and empirical performance, the automated bHP filtering procedure is well suited as a general-purpose machine for low-frequency macroeconomic time series.
    Chair Xinyu Zhang Chinese Academy of Sciences
    Hosts TEDS and Chinese Academy of Sciences

    Prof. SHIMOTSU Katsumi Date April/26/2022
    Time Japan Standard Time (UTC+9h)
    10:50 ~ 12:20
    Speaker Katsumi Shimotsu The University of Tokyo
    Title Testing the Order of Multivariate Normal Mixture Models
    Abstract Finite mixtures of multivariate normal distributions have been widely used in empirical applications in diverse fields such as statistical genetics and statistical finance. Testing the number of components in multivariate normal mixture models is a long-standing challenge even in the most important case of testing homogeneity. This paper develops a likelihood-based test of the null hypothesis of M components against the alternative hypothesis of M+1 components for a general M>=1. We derive the asymptotic distribution of the proposed EM test statistic under the null hypothesis and local alternatives and show the validity of parametric bootstrap. The simulations show that the proposed test has good finite sample size and power properties.
    Zoom Click here to join the Zoom Meeting Meeting ID: 857 7302 7215 Passcode: 674245
    Venue Koganei Campus, Hosei University. If you want to join in person, please contact
    Chair Qingfeng Liu Hosei University
    Hosts TEDS and Hosei University

    Prof. Jinchi Lv Date April/12/2022
    Time Japan Standard Time (UTC+9h)
    10:00 ~ 11:30
    Speaker Jinchi Lv University of Southern California
    Title High-Dimensional Knockoffs Inference for Time Series Data
    Abstract The recently introduced framework of model-X knockoffs provides a flexible tool for exact finite-sample false discovery rate (FDR) control in variable selection in arbitrary dimensions without assuming any dependence structure of the response on covariates. It also completely bypasses the use of conventional p-values, making it especially appealing in high-dimensional nonlinear models. Existing works have focused on the setting of independent and identically distributed observations. Yet time series data is prevalent in practical applications in various fields such as economics and social sciences. This motivates the study of model-X knockoffs inference for time series data. In this paper, we make some initial attempt to establish the theoretical and methodological foundation for the model-X knockoffs inference for time series data. We suggest the method of time series knockoffs inference (TSKI) by exploiting the idea of subsampling to alleviate the difficulty caused by the serial dependence. We establish sufficient conditions under which the original model-X knockoffs inference combined with subsampling still achieves the asymptotic FDR control. Our technical analysis reveals the exact effect of serial dependence on the FDR control. To alleviate the practical concern on the power loss because of reduced sample size cause by subsampling, we exploit the idea of knockoffs with copies and multiple knockoffs. Under fairly general time series model settings, we show that the FDR remains to be controlled asymptotically. To theoretically justify the power of TSKI, we further suggest the new knockoff statistic, the backward elimination ranking (BE) statistic, and show that it enjoys both the sure screening property and controlled FDR in the linear time series model setting. The theoretical results and appealing finite-sample performance of the suggested TSKI method coupled with the BE are illustrated with several simulation examples and an economic inflation forecasting application. This is a joint work with Chien-Ming Chi, Yingying Fan and Ching-Kang Ing.
    Video Click here to watch the video
    Chair Yoshimasa Uematsu Hitotsubashi University
    Hosts TEDS and Hitotsubashi University

    Other 2021 seminars (with videos and slides)

    2022 New Year TEDS Speech

    Image by monicore from Pixabay
  • 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 Image by monicore from Pixabay
    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

    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.