2020 Spring Awards

Uconn sealAlthough the department was not able to celebrate with an awards banquet this year, we still are able to recognize the best among undergraduate and graduate students, as well as faculty! This year’s award recipients are:

Undergraduate Awards

Economics Department General Scholarship

Yulia Bragina
Tyler DiBrino
Kevin Fiddler
Melissa Mendez
Sueing Ngov
Shannon O’Connor
Gabriela Rodriguez

Kathryn A. Cassidy Economics Scholarship

Kelly-Anne Moffa
Katelyn Mooney

Rockwood Q. P. Chin Scholarship

Kevin Fiddler
Devin Pallanck
Gabriela Rodriguez
Yumeng Shao

Louis D. Traurig Scholarship

Marisa Infante
Spencer Kinyon
Linge Yang
Ajshe Zulfi

Albert E. Waugh Scholarship

Xiaofeng Gong

Paul N. Taylor Memorial Prize

John Peterson

Julia & Harold Fenton and Yolanda & Augustine Sineti Scholarship

Tyler DiBrino

Charles Triano Scholarship

Tiffany D’Andrea
James Rice

Dr. Joseph W. McAnneny Jr. Scholarship

Madeline Danziger
Zian Zhang

Robert J. Monte Scholarship

Luis Cruz

Ross Mayer Scholarship

Michelle Grieco
Mary Vlamis

Graduate Awards

W. Harrison Carter Award

Lindsey Buck

Abraham Ribicoff Graduate Fellowship

Yangkeun Yun

Timothy A. and Beverly C. Holt Economics Fellowship

Yijia Gao
Xuejian Gong
Ruohan Huang
Ha Kyeong Lee
Wensu Li
Miranda Mendiola Valdez
Ziyun Wu

Economics Department General Scholarship

Jingyun Chen
Chun Li
Jinning Wang
Heli Xu

Best Third Year Paper Award

Erdal Asker
Deepak Saraswat

Graduate School Pre-Doctoral Fellowship

Treena Goswami
Huarui Jing
Anastassiya Karaban
Xizi Li
Shilpa Sethia
Rui Sun
Jiaqi Wang
Wei Zheng

Faculty Awards

Grillo Family Research Award

Jungbin Hwang

Grillo Family Teaching Award

Delia Furtado


Congratulations to everyone!

High Dimensional Econometrics at the NE Statistics Symposium (NESS)

The Department of Economics will be sponsoring a session at the 33rd New England Statistics Symposium (NESS) on May 15–17, 2019.

High Dimensional Econometrics

The technological innovations in information processing and the increased storage capability have made possible to collect very large data sets in various fields of economics and finance.

This session puts together 3 papers that present state-of-the-art techniques to deal with high dimensional issues in econometrics.

List of invited speakers:

(1) Fa Wang, Cass Business School, Fa.Wang@city.ac.uk, “Maximum Likelihood Estimation and Inference for High Dimensional Nonlinear Factor Models with Application to Factor-augmented Regressions”

(2) Yuan Liao, Rutgers Economics, yuan.liao@rutgers.edu, “Inference for Heterogeneous Effects Using Low Rank Estimation”

(3) Min Seong Kim, UConn Economics, min_seong.kim@uconn.edu, “Policy Analysis Using Panel and Multilevel Models with Group Interactive Fixed Effects”

Discussant: Jungbin Hwang, UConn Economics, jungbin.hwang@uconn.edu

Session Chair: Chihwa Kao, UConn Economics, chih-hwa.kao@uconn.edu

Information about the conference may be found online at https://symposium.nestat.org/

Professor Jungbin Hwang to be Published in the Journal of Econometrics

Professor Jungbin Hwang’s paper “Should We Go One Step Further? An Accurate Comparison of One-step and Two-step Procedures in a Generalized Method of Moments Framework”, co-authored with Yixiao Sun, has been accepted for publication in the Journal of Econometrics, one of the top scholarly journals in theoretical econometrics. The paper started as a third-year paper project when Professor Hwang was a graduate student in the University of California, San Diego.

Professor Hwang’s paper provides an assessment of the merits of the first step GMM estimator and test relative to the two-step GMM estimator and test. The article shows the two-step procedure outperforms the one-step method only when the benefit of using the optimal weighting matrix outweighs the cost of estimating it. The qualitative message applies to both the asymptotic variance comparison and power comparison of the associated tests.


According to the conventional asymptotic theory, the two-step Generalized Method of Moments (GMM) estimator and test perform as least as well as the one-step estimator and test in large samples. The conventional asymptotic theory, as elegant and convenient as it is, completely ignores the estimation uncertainty in the weighting matrix, and as a result it may not reflect finite sample situations well. In this paper, we employ the fixed-smoothing asymptotic theory that accounts for the estimation uncertainty, and compare the performance of the one-step and two-step procedures in this more accurate asymptotic framework. We show that the two-step procedure outperforms the one-step procedure only when the benefit of using the optimal weighting matrix outweighs the cost of estimating it. This qualitative message applies to both the asymptotic variance comparison and power comparison of the associated tests. A Monte Carlo study lends support to our asymptotic results.


Professor Jungbin Hwang to be Published in Econometric Theory

Professor Jungbin Hwang and his co-author Yixiao Sun have had their paper, “Simple, Robust, and Accurate F and t Tests in Cointegrated Systems,” accepted by Econometric Theory  as a lead article in a future issue.

In this paper, they propose new, simple, and more accurate statistical tests in a cointegrated system that allows for endogenous regressors and serially dependent errors. The approach involves first transforming the time series using orthonormal basis functions in L-2 space, which have energy concentrated at low frequencies, and then running an augmented regression based on the transformed data and constructing the test statistics in the usual way. The F and t tests developed in this article, are extremely simple to implement have more accurate size in finite samples than existing tests such as the asymptotic chi-squared and normal tests based on the fully modified OLS estimator of Phillips and Hansen (1990) and can be made as powerful as the latter test.

Professor Hwang Publishes in Journal of Econometrics

Professor Jungbin Hwang has published the paper “Asymptotic F and t tests in an efficient GMM setting” with his co-author Yixiao Sun in the Journal of Econometrics Volume 198, Issue 2, June 2017, Pages 277-295.

In this paper, they propose a simple and easy-to-implement modification to the trinity of test statistics in the two-step efficient GMM setting and show that the modified test statistics are all asymptotically F distributed under the so-called fixed-smoothing asymptotics. The main contributions of this paper are developing convenient asymptotic F tests whose critical values, i.e., the standard F critical values, are readily available from standard statistical tables and programming environments. For testing a single restriction with a one-sided alternative, the paper also develops an asymptotic test theory using the standard t distribution as the reference distribution.

New Faculty Join Economics Department

The Economics Department is happy to welcome four faculty who joined UConn at the beginning of the Fall Semester.  Chihwa (Duke) Kao, formerly at Syracuse University, joined the economics department as its new Department Head.  Kao is a renowned econometrician working on time series and panel data topics.

Jungbin Hwang also joined the faculty as an Assistant Professor this Fall after completing his Ph.D. at the University of California San Diego.  Hwang is also an econometrician working on panel data and time series topics.

Hyun Lee also joins the faculty as an Assistant Professor after completing his Ph.D. at the University of Chicago.  Hyun is a macroeconomist who works on topics related to economic growth and policy analysis.

Patricia Ritter joins the faculty as an Assistant Professor following completion of her Ph.D. at the University of Chicago.  Dr. Ritter works on topics at the intersection of development and health.