We are delighted to share that three of our 5th-year PhD students focusing in econometrics, Xuejian Gong, Ruohan Huang, and Ziyun Wu, recently accepted (full-time) job offers in the US financial industry.
Xuejian has accepted a job offer as assistant vice president for wholesale credit risk management at Citi Institutional Clients Group. His dissertation (advised by Professor Duke Kao) is about applying distributionally robust optimization in economic and financial models. Ruohan has started her career at OneMain General Services Corporation as a senior analyst in credit, pricing, and analytics. The subject of her dissertation (advised by Professor Jungbin Hwang) is financial econometrics, focusing on empirical asset pricing models. Lastly, Ziyun has started work as a data scientist at Hartford Steam Boiler – Munich Re. Her dissertation (advised by Professor Duke Kao) studies the machine learning approach in asset pricing.
All three students commented that their programming language skills and understanding of various econometrics/statistical theories from their PhD training were key factors for their success in the job market. Also, they pointed out the importance of earlier preparations for the industry job market, as most companies for quantitative analyst positions have at least two rounds of interviews for coding and critical thinking.
We are again pleased to congratulate our PhD students’ achievements in their job markets and know that they will have great success in their careers in financial industries!
In many applications of econometrics and economics, a large proportion of the questions of interest are identification. An economist may be interested in uncovering the true signal when the data could be very noisy, such as time-series spurious regression and weak instruments problems, to name a few. In this book,High Dimensional Econometrics and Identification, we illustrate the true signal and, hence, identification can be recovered even with noisy data in high-dimensional data, e.g., large panels. High-dimensional data in econometrics is the rule rather than the exception. One of the tools to analyze large, high-dimensional data is the panel data model.
High Dimensional Econometrics and Identificationgrew out of research work on the identification and high-dimensional econometrics that we have collaborated on over the years, and it aims to provide an up-to-date presentation of the issues of identification and high-dimensional econometrics, as well as insights into the use of these results in empirical studies. This book is designed for high-level graduate courses in econometrics and statistics, as well as used as a reference for researchers.
Panel Data Model with Stationary and Nonstationary Regressors and Error Terms
Panel Time Trend Model with Stationary and Nonstationary Error Terms
Estimation of Change Points in Stationary and Nonstationary Regressors and Error Term
Weak Instruments in Panel Data Models
Incidental Parameters Problem in Panel Data Models
Readership: Graduate and researchers in the field of econometrics and economics.
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.