BMET 27A33 - Introduction to Econometrics Using R

This course aims to teach the fundamentals of econometrics, in particular regression analysis and statistical inference. We purposefully start at a level that assumes no prior knowledge of statistics, econometrics or programming. Our objective is to have you understand and interpret simple and multiple linear regressions and able to detect whether an analysis uncovers correlations or causality. This class does not rely on advanced maths, rather it uses practical learning in order to teach the main concepts. Moreover, in this course you will learn to use R, a very powerful and widely used programming language, to perform data cleaning and undertake statistical analyses. Our hope is that by the end of the course you are able to carry out your own small-scale research project using real-world data and regression analysis to reveal associations between various variables.

All the slides for this class can be found here.

During the course, participants will have the opportunity

to take part in various stages of preparing data for statistical and econometric analysis including collecting, merging, transforming, and cleaning of raw data;

to identify and highlight key features of data through the production of summary statistics as well as informative data plots;

to carry out multivariate linear regression analysis including

the understanding of the key features of Ordinary Least Squares estimation;

the knowledge of the assumptions underlying the statistical reliability of regression coefficient estimates;

the statistical and economic interpretation of coefficient estimates and associated standard error estimates as well as other model statistics;

the choice of control variables to include in the analysis;

the knowledge of the conditions under which regression coefficients can be interpreted as causal in the sense of the Neyman-Rubin model of potential outcomes;

to learn the programming language R.

None

• Moodle Quizzes: 40% (5 assignments, each worth 8%
• Midterm case-study 30%
• Final research project 30%