This course introduces the main topics in Econometrics by using R statistical software. The relation of themes is comprehensive and includes the basic notions such as linear regression, multiple regression, causal inference, regression discontinuity and instrumental variable. In total, the course covers thirteen chapters that are common in any undergraduate econometrics course.
The use of statistical methods in economics is known as Econometrics. This branch of Economics is the backbone of empirical economics that is taught in most undergraduate programs. Since econometrics requires data, there is a huge discussion around what kind of methods are suited to deal with the different types of data and problems that a scholar wants to solve. In general, there are cross-sectional data and panel data. The first one collects information from different units (which could be individuals, households, firms, etc) at a given point in time, whereas the second one follows a set of units through more than one period.
One o f the main challenges in Econometrics is to achieve meaningful or generalizable results from limited datasets. Ultimately, collecting all the information from an entire population is costly and in general impossible. Another challenge is the ability to identify and assess causality relationships among variables. The course which is offered here allows an understanding of the main topics in Econometrics such as linear regression, multiple regression, and panel data methods.
The complexity of situations that involve the fact that data not always accomplish desired features make Econometrics a field in which ingenuity and technique must be combined. Of course, Econometric methods are no free from controversy, so it is important to mention there are authors that critically have discussed the main corpus of Econometrics. For instance Regression Analysis: A constructive Critique, written by Richard A. Berk.
