Next Seminar

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

Past Seminars

Prof. Qihui Chen Date April/14/2023
Time Japan Standard Time (UTC+9h)
Speaker Qihui Chen The Chinese University of Hong Kong (Shenzhen)
Title Semiparametric Conditional Factor Models: Estimation and Inference
Abstract This paper introduces a simple and tractable sieve estimation of semiparametric conditional factor models with latent factors. We establish large-N-asymptotic properties of the estimators and test statistics without requiring large T. We also develop a simple bootstrap procedure for conducting inference about the conditional pricing errors as well as the shapes of the factor loading functions. These results enable us to estimate the conditional factor structure of a large set of individual assets by utilizing arbitrary nonlinear functions of a number of characteristics without the need to pre-specify the factors, while allowing us to disentangle the characteristics’ role in capturing factor betas from alphas (i.e., undiversifiable risk from mispricing). We apply these methods to the cross-section of individual U.S. stock returns and find strong evidence of large nonzero pricing errors that combine to produce arbitrage portfolios with Sharpe ratios above 3.
Venue Click here to join the Zoom Meeting Meeting ID:852 1615 0902 Passcode: 207686
Chair Wenjie Wang Nanyang Technological University
Hosts TEDS and Nanyang Technological University

Prof. Gregory Cox Date March/31/2023
Time Japan Standard Time (UTC+9h)
Speaker Gregory Cox National University of Singapore
Title A Conditional Likelihood Ratio Test for the Timing of a Structural Break
Abstract Time series often have a structural break at some point in the sample. This paper proposes a new hypothesis test, the conditional likelihood ratio (CLR) test, for the timing of a structural break in the coefficients of a linear regression. Since inference on the timing of a break depends critically on the magnitude of the break, the CLR test uses a critical value from the conditional distribution of the likelihood ratio statistic given a sufficient statistic for the magnitude of the break. To estimate the conditional critical value, we develop a specially designed null-imposed bootstrap. We prove that the CLR test is uniformly valid over the magnitude of the break. Simulations indicate the confidence set for the break date formed by inverting the CLR test has good coverage probability for any break magnitude and shorter average length than alternative confidence sets.
Venue Click here to join the Zoom Meeting Meeting ID: 840 5821 5629 Passcode: 067622
Chair Wenjie Wang Nanyang Technological University
Hosts TEDS and Nanyang Technological University

Prof. Yu Zhou Date March/22/2023
Time Japan Standard Time (UTC+9h)
Speaker Yu Zhou East China Normal University
Title Deep Nonlinear Sufficient Dimension Reduction
Abstract Undergoing accelerated developments for more than 30 years, sufficient dimension reduction (SDR) has now become a powerful tool in statistics. We in this paper establish a general framework to take the advantage of deep neural networks (DNNs) to perform nonlinear sufficient dimension reduction. Compared to existing methods based on RKHS, our proposal is more efficient in computation. We also systematically study the convergence rate of the sample estimator, which is nearly minimax optimal. Comprehensive simulation studies and real data applications demonstrate the effectiveness of the propose method.
Video Click here to watch the video
Chair Wenjie Wang Nanyang Technological University
Hosts TEDS, JSPS KAKENHI (B) No. 22H00833 and Center for Quantitative Economics of Jilin University.

Prof.Ming Li Date March/10/2023
Time Japan Standard Time (UTC+9h)
Speaker Ming Li National University of Singapore
Title Identification and Estimation in a Time-Varying Endogenous Random Coefficient Model
Abstract This paper proposes a random coefficient panel data model in which the regressors are correlated with the time-varying random coefficients in each period. We model the random coefficients as unknown functions of a fixed effect of arbitrary dimension, a time-varying random shock that affects the values of regressors, and an idiosyncratic shock. We introduce a new panel data-based identification strategy to control for the correlation between the time-varying random coefficients and regressors. We propose a three-step series estimator that is easy to compute and prove that it is asymptotically normal. Simulation results show that the proposed method accurately estimates the first-order moments of the random coefficients. As an empirical illustration, we estimate unconditional and conditional means of output elasticities with respect to capital and labor for the five largest sectors of China's manufacturing industry and find significant across-firm variation in the output elasticities within each sector.
Venue Click here to join the Zoom Meeting Meeting ID:845 2990 2373 Passcode: 070249
Chair Wenjie Wang Nanyang Technological University
Hosts TEDS and Nanyang Technological University

Prof. Robin L. Lumsdaine Date January/26/2023
Time Japan Standard Time (UTC+9h)
Speaker Robin L. Lumsdaine American University
Title Central Bank Mandates and Monetary Policy Stances: through the Lens of Federal Reserve Speeches
Abstract When does the Federal Reserve deviate from its dual mandate of pursuing the economic goals of maximum employment and price stability and what are the consequences? We assemble the most comprehensive collection of Federal Reserve speeches to-date and apply state-of-the-art natural language processing methods to extract a variety of textual features from each paragraph of each speech. We find that the periodic emergence of non-dual mandate related discussions is an important determinant of time-variations in the historical conduct of monetary policy with implications for asset returns. The period from mid-1996 to late-2010 stands out as the time with the narrowest focus on balancing the dual mandate. Prior to the 1980s there was a outsized attention to employment and output growth considerations, while non dual-mandate discussions centered around financial stability considerations emerged after the Great Financial Crisis. Forward-looking financial stability concerns are a particularly important driver of a less accommodative monetary policy stance when Fed officials link these concerns to monetary policy, rather than changes in banking regulation. Conversely, discussions about current financial crises and monetary policy in the context of inflation-employment themes are associated with a more accommodative policy stance.
Video Click here to watch the video
Chair Wenjie Wang Nanyang Technological University
Hosts TEDS and Nanyang Technological University

Prof. Toru Kitagawa Date December/8/2022
Time Japan Standard Time (UTC+9h)
Speaker Toru Kitagawa Brown University and University College London
Title Policy Choice in Time Series by Empirical Welfare Maximization
Abstract This paper develops a novel method for policy choice in a dynamic setting where the available data is a multivariate time series. Building on the statistical treatment choice framework, we propose Time-series Empirical Welfare Maximization (T-EWM) methods to estimate an optimal policy rule for the current period or over multiple periods by maximizing an empirical welfare criterion constructed using nonparametric potential outcome time-series. We characterize conditions under which T-EWM consistently learns a policy choice that is optimal in terms of conditional welfare given the time-series history. We then derive a non-asymptotic upper bound for conditional welfare regret and its minimax lower bound. To illustrate the implementation and uses of T-EWM, we perform simulation studies and apply the method to estimate optimal monetary policy rules from macroeconomic time-series data.
Video Click here to watch the video
Chair Ryo Okui The University of Tokyo
Hosts TEDS, JSPS KAKENHI (B) No. 22H00833 (Liu).

Prof. Koen Jochmans Date November/24/2022
Time Japan Standard Time (UTC+9h)
Speaker Koen Jochmans Toulouse School of Economics
Title Bootstrap inference for fixed-effect models
Abstract The maximum-likelihood estimator of nonlinear panel data models with fixed effects is asymptotically biased under rectangular-array asymptotics. The literature has devoted substantial effort to devising methods that correct for this bias as to salvage standard inferential procedures. The chief purpose of this paper is to show that the (recursive, parametric) bootstrap replicates the distribution of the (uncorrected) maximum-likelihood estimator in large samples. This justifies the use of confidence sets constructed via conventional bootstrap methods. No adjustment for the presence of bias needs to be made.
Video Click here to watch the video
Chair Ryo Okui The University of Tokyo
Hosts TEDS, JSPS KAKENHI (B) No. 22H00833 (Liu).

Prof.Yohei Yamamoto Date November/8/2022
Time Japan Standard Time (UTC+9h)
Speaker Yohei Yamamoto Hitotsubashi University
Title Anthropogenic influence on extremes and risk hotspots
Abstract Study of the frequency and magnitude of climate extremes as the world warms is of utmost importance, especially separating the influence of natural and anthropogenic forcing factors. Record-breaking temperature and precipitation events were studied using event-attribution techniques. Here, we provide spatial and temporal observation-based analyses of the role of natural and anthropogenic factors, using state-of-the-art time series methods. We show that the risk from extreme temperature and rainfall events has severely increased for most regions worldwide. We also identify risk hotspots defined as regions for which increased risk of extreme events and high exposure in terms of either high Gross Domestic Product (GDP) or large population are both present. For the year 2018, increased anthropogenic forcings are mostly responsible for increased risk to extreme temperature/precipitation affecting 94%/72% of global population and 97%/76% of global GDP relative to the baseline period 1961-1990.
Video Click here to watch the video
Chair Wenjie Wang Nanyang Technological University
Hosts TEDS and Nanyang Technological University

Prof.Jungbin Hwang Date October/14/2022
Time Japan Standard Time (UTC+9h)
10:00 ~ 11:30
Speaker Jungbin Hwang University of Connecticut
Title Fixed-Cluster Inference with Unbalanced Cluster Sizes
Abstract This paper provides asymptotic distribution and approximation theory for clustered data with a fixed number of large-sized clusters, allowing cluster sizes to be heterogeneous. In developing new inference procedures under fixed-cluster asymptotics, we make the following contributions. First, we show that the fixed-cluster asymptotic distribution of cluster-robust Wald statistic can be represented by a quadratic form of a standard normal vector with an independent random weighting matrix, where the random weighting matrix is equal in distribution to a weighted sum of independent Wishart matrices. Second, we develop a convenient F-approximation to the nonstandard fixed-cluster limiting distribution. The key intuition is that a scaled Wishart matrix can well approximate the random weighting matrix in the fixed-cluster limit of the Wald statistic with a proper choice of degree of freedom. We also show that the equivalent degree of freedom in our F-approximation connects to the equivalent number of clusters in Carter et al.’s (2013). Lastly, we establish the fixed-G asymptotic distribution and F-approximation for the test based on the jackknife CCE and prove its built-in bias adjustment function.
Venue Click here to join the Zoom Meeting Meeting ID:869 1606 0736 Passcode:104168
Chair Wenjie Wang Nanyang Technological University
Hosts TEDS and Nanyang Technological University

Prof. Martin Weidner Date September/29/2022
Time Japan Standard Time (UTC+9h)
Speaker Martin Weidner University of Oxford
Title Bounds on Average Effects in Discrete Choice Panel Data
Abstract Average effects in discrete choice panel data models with individual-specific fixed effects are generally only partially identified in short panels. While consistent estimation of the identified set is possible, it generally requires very large sample sizes, especially when the number of support points of the observed covariates is large, such as when the covariates are continuous. In this paper, we propose estimating outer bounds on the identified set of average effects. Our bounds are easy to construct, converge at the parametric rate, and are computationally simple to obtain even in moderately large samples, independent of whether the covariates are discrete or continuous. We also provide asymptotically valid confidence intervals on the identified set. Simulation studies confirm that our approach works well and is informative in finite samples. We also consider an application to labor force participation.
Video Click here to watch the video
Chair Ryo Okui The University of Tokyo
Hosts TEDS, JSPS KAKENHI (B) No. 22H00833 (Liu).

Prof. Wenjie Wang Date September/28/2022
Time Japan Standard Time (UTC+9h)
Speaker Wenjie Wang Nanyang Technological University
Title Wild Bootstrap Inference for Instrumental Variables Regressions with Weak and Few Clusters
Abstract We study the wild bootstrap inference for instrumental variable regressions with a small number of large clusters. We first show that the wild bootstrap Wald test controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters and has power against local alternatives if the parameters of endogenous variables are strongly identified in five or six clusters. We further develop a wild bootstrap Anderson-Rubin test for the full-vector inference and show that it controls size asymptotically even under weak identification in all clusters. We illustrate their good performance using simulations and provide an empirical application to a well-known dataset about US local labor markets.
Venue Click here to join the Zoom Meeting Meeting ID: 9625852673
Chair Dayu Liu Jilin University
Hosts TEDS and Center for Quantitative Economics of Jilin University

A/Prof. Yang Feng Date August/27/2022
Time Japan Standard Time (UTC+9h)
Speaker Yang Feng New York University
Title Transfer Learning under High-dimensional Generalized Linear Models
Abstract In this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful source data. Given which sources to transfer, we propose a transfer learning algorithm on GLM, and derive its ℓ1/ℓ2-estimation error bounds as well as a bound for a prediction error measure. The theoretical analysis shows that when the target and source are sufficiently close to each other, these bounds could be improved over those of the classical penalized estimator using only target data under mild conditions. When we don't know which sources to transfer, an algorithm-free transferable source detection approach is introduced to detect informative sources. The detection consistency is proved under the high-dimensional GLM transfer learning setting. We also propose an algorithm to construct confidence intervals of each coefficient component, and the corresponding theories are provided. Extensive simulations and a real-data experiment verify the effectiveness of our algorithms. We implement the proposed GLM transfer learning algorithms in a new R package glmtrans, which is available on CRAN.
Venue Click here to join the Zoom Meeting Meeting ID: 9625852673
Chair Han Liu Jilin University
Hosts TEDS and Center for Quantitative Economics of Jilin University

Speaker Bruce E. Hansen University of Wisconsin-Madison
Date August/26/2022
Time Japan Standard Time (UTC+9h)
10:00 ~ 11:30
Video Click here to watch the video
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.

    Prof. Degui Li Date August/19/2022
    Time Japan Standard Time (UTC+9h)
    Speaker Degui Li University of York
    Title Estimating Time-Varying Networks for High-Dimensional Time Series
    Abstract We explore time-varying networks for high-dimensional locally stationary time series, using large VAR with both transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are considered: one containing directed edges of Granger causality linkages, and the other containing undirected edges of partial correlation linkages. Under the sparse structural assumption, we propose a penalised local linear estimation method with weighted time-varying (or group) LASSO to jointly estimate the transition matrices and identify their significant entries, and a time-varying CLIME method to estimate the precision matrix. The estimated transition and precision matrices are then used to determine the time-varying network structures. Under some mild conditions, we derive the theoretical properties of the proposed estimates including the oracle and consistency properties. Extensive simulation studies are provided to illustrate the finite-sample performance.
    Venue Click here to join the Zoom Meeting Meeting ID: 846 1570 0936 Passcode: 403626
    Chair Qu Feng Nanyang Technological University
    Hosts TEDS and Nanyang Technological University

    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)

    Other 2022 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 (B) No. 22H00833 (Qingfeng Liu), Nanyang Technological University, Joint Research Program of KIER, Kyoto University and Chinese Academy of Sciences.