Data Analytics and Regression for Executives

Catalog description

This course introduces students to the study of modern econometric techniques employed in economics and policy analysis. For example, these techniques have been used to answer questions like: Does increasing the minimum wage raise unemployment? Do employers discriminate based on names? Does a shorter workweek affect productivity? Are unconditional cash transfers effective at reducing poverty? During the course we will study the basic linear regression model, learn how to estimate multivariate relationships in a data sample, and test hypothesis about the underlying population. We will then consider situations where linear regressions can go wrong, and what to do about it. Examples include measurement error, selection on unobservables, and omitted variables. We will also introduce time- series and forecasting techniques. The course will end with a brief introduction to panel data techniques and regression discontinuity techniques, which are important in modern empirical policy analysis. Throughout the course we will discuss how these techniques can be used to conduct policy analysis as well as the potential problems and pitfalls with doing so. The course will cover both theoretical and practical issues, and problem sets will contain applications to real data and require the use of Python. Gaining additional experience and expanding your Python toolkit will also be helpful for your future studies because many elective courses require the use of Python. You will also have first-hand experience working on a research paper of your own, where you will be guided through the process of finding adequate data, data cleaning, analysis and presentation.