Empirical Methods for Finance


The objective of the course is to introduce students to empirical research methods and data analysis in finance. The nature, scope, and detail of available data continue to expand rapidly and there is growing demand in the financial industry for people with quantitative skills who are able to analyze them. The course is designed to help you understand and apply some standard techniques used in the econometric analysis of financial data thorugh a combination of face-to-face and online lectures. The course will emphasize solid foundations with a focus on empirical applications. The course starts with the basics of regression analysis to finally cover some of the most widely used methodologies in finance to draw causal conclusions, such as event studies and difference-in-differences. You will be introduced to programming in a widely used statistical software (Stata) through applications of the concepts and methods discussed in class. You will be assigned one graded group exercises that require dealing with data, estimating models and interpreting the results. The code for the solutions will be in Stata. By implementing the methods learned in class on the data you will appreciate the empirical issues and the intuition behind the adopted econometric approaches.

General characterization





Responsible teacher

Robert Anthony Hill


Weekly - Available soon

Total - Available soon

Teaching language





Brooks, C. (2019) Introductory Econometrics for Finance, 4th edition. Cambridge University Press, New York 

Wooldridge, J.M. (2013). Introductory econometrics: A modern approach (5th edition). Mason, OH: South-Western, Cengage Learning

Teaching method

This is a blended course. During the in-person lectures we will cover the core material and discuss examples. The asynchronous videos go through the Stata applications showing and explaining the code (data and code are always provided). You are encouraged to have Stata open on your computer and run the code yourself while watching the video: the asynchronous format is very well suited for this purpose. By looking at several examples of common empirical problems you will develop an intuition for what methods are most suited to overcome specific challenges faced when working with data. By the end of the course, you will be able to write simple codes, to assess the validity of the approach used and to discuss the results of linear regression models. Pre-class preparation and in-class participation is expected.

Evaluation method

Students will be assessed on two assignments (40%) and a final exam (60%).

Subject matter

Univariate and Multivariate Regression Analysis

Linear regression model

Ordinary Least Square (OLS)

Inference Special regressors (dummy variables and interaction terms)

Specification issues

Regression diagnostics