Workshop on Econometrics and Statistics with professor Jianqing Fan
Jianqing Fan (Princeton University) gave the keynote lecture entitled “When Can Weak Latent Factors Be Statistically Inferred?” at a workshop in Econometrics and Statistics organized by the Department of Economics and Statistics. Professor Fan is a distinguished researcher in statistics, finance, and machine learning. The interdisciplinary workshop promoted an exchange of ideas between economists and statisticians on how cutting-edge statistical methods can be applied to analyze complex economic data.
On June 2, a workshop on econometrics and statistics gathered researchers in the fields of statistics and econometrics at Handelshögskolan. Organizer Andreas Dzemski (Department of Economics and Statistics) explains that the interdisciplinary event aims to promote the exchange of ideas between economists and statisticians on how cutting-edge statistical methods can be applied to analyze complex economic data. The workshop started with a keynote lecture entitled "When Can Weak Latent Factors Be Statistically Inferred?" by Jianqing Fan (Princeton), a distinguished researcher in statistics, finance, and machine learning.
Dzemski says: "Prof Fan is a giant in the fields of statistics and econometrics and a true visionary. We are honored to have him here to share his views on the most important challenges in modern data analysis."
The keynote was followed by presentations of researchers in econometrics and statistics. "I'm very happy with how the workshop turned out," Dzemski adds. "We have already started discussing how to organize the next one. I'm especially pleased that we could involve so many young researchers, including PhD students, in the event. Being able to network is crucial for their careers."
The workshop has special significance to the Department of Economics and Statistics. "We have many researchers who either develop new statistical methods or who apply existing statistical methods to economic data," Dzemski explains. "Modern methods provide new research opportunities, especially for researchers analyzing large datasets ('big data') or researcher who work with non-traditional kinds of data, such as textual data or social network data. Keeping up with the latest developments is crucial for us."
Students are wondering how AI and machine learning methods relate to more traditional econometric methods. Workshops like this one help us answer such questions.
Insight into modern methods is also important for what is taught in the classroom, Dzemski adds: "Students are wondering how AI and machine learning methods relate to more traditional econometric methods. Workshops like this one help us answer such questions." The workshop was funded by Jan Wallanders och Tom Hedelius stiftelse samt Tore Browaldhs stiftelse.