This summer school will cover fundamental topics in econometrics and data science. The content ranges from predictive and causal methods for time-series analysis, to state-space methods and filtering techniques for high-dimensional datasets. Participants will learn how to design, test and evaluate quantitative models and methods in Business, Economics and Finance.
Given the interdisciplinary nature of the summer school, we will begin with a review of basic methods in econometrics, data science, structural modeling and time series. Practical cases are developed for different purposes in the fields of business, economics, and finance. For each topic, we cover both the theory and methodology, as well as hands-on applications with real data. In particular, we illustrate their use and their importance for all practical purposes, we implement the basic methods in a computer lab, and we assess their performance in a real data setting.
Participants will work in small groups to develop (a) structural models for the support of marketing and pricing decisions in business, (b) designing time series models for macroeconomic forecast, (c) a case on extracting and forecasting signals from noisy business data using the Kalman filter, and (d) a case on incorporating vast data resources for measuring and nowcasting current economic activity.