Data Analytics II: Advances in Economics and Finance (EC989).

Ping’s website.

Luigi’s website.

Overview.

The abundance of time series data in economics and finance has sparked a growing interest in flexible data analytical techniques that efficiently handle data and identify meaningful patterns in time series. This module primarily focuses on statistical and machine learning tools that are increasingly used by practitioners, econometricians, data analysts and forecasters in central banks, government statistical agencies, policy institutions, or the private sector. It is designed to provide students with an in-depth understanding and application of advanced time series econometrics by covering topics such as linear and non-linear time series regressions, textual inference, as well as popular machine and statistical learning techniques in macroeconomics and finance. The module establishes a strong and closer link between students and the advanced and modern time series econometric tools used by data scientists and analysts for inference in macroeconomics and finance.

List of lectures and lab sessions.

1. Recap of linear time series data analytics
2. Text as data: Textual inference in macroeconomics and finance
3. Non-linear time series data analytics I: Markov switching
4. Non-linear time series data analytics II: Time-varying parameter regressions and volatility modeling
5. Non-linear time series data analytics III: Machine learning techniques using tree-based methods and deep learning techniques