Course Description

This course offers an in-depth exploration of econometric techniques and tools essential for analyzing economic data, with a particular focus on addressing business problems through quantitative data analysis methods. Students will gain hands-on experience in the data analysis process, starting from problem analysis to identify data requirements and analysis needs, applying appropriate data analysis methods, and interpreting results within the given context. The course covers a range of topics from basic mathematical tools to advanced regression methods, equipping students with the skills needed to conduct rigorous empirical research. Emphasis is placed on understanding the underlying assumptions and limitations of each method, as well as practical applications using real-world data. Data analysis software such as spreadsheets and R will be utilized to support skill-building throughout the course. By the end of the course, students will be proficient in various econometric techniques, capable of addressing data irregularities, and familiar with advanced topics like machine learning tools.

Learning Objectives

At the end of the course, students should achieve the following capabilities:

• Learn the fundamentals of Ordinary Least Squares (OLS) regression and understand how to interpret and address standard errors.

• Comprehend the potential outcomes framework and its application in causal inference.

• Implement matching and subclassification techniques to address selection bias in observational studies.

• Recognize common data irregularities and apply appropriate methods to correct them.

• Learn to apply fixed effects models to control for unobserved heterogeneity.

• Apply probability models such as logit and probit to binary outcome data.

• Understand the principles of regression discontinuity design and its application in causal inference.

• Learn to use instrumental variables to address endogeneity in regression models.

• Identify and correct for measurement errors in econometric models.

• Apply difference-in-difference methods to evaluate treatment effects in panel data.

• Develop practical skills in data analysis, including identifying data requirements, applying analysis methods, and interpreting results.

• Use R or Stata to support data analysis and skill-building

Prerequisite(s)

Background in Statistics