Course Introduction: 

You may think that the nature of the research that you do or that you plan to do makes the econometric tools unnecessary and overly complicated. It is safe to say that is very very likely not the case. Any economic research that has an empirical component requires working with data. None of you will make use of all the tools we will explore. The course, however, aims to provide you with some overview of available methods and guidance when and how to use them.

Given that you come from various backgrounds, some of you may different parts of the course elementary, others, to the contrary, overwhelming. I try to make it such that both groups could potentially benefit from the lectures.

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Linear regression is arguably the most important tool in econometrics.

The first day is a short crash course on a Linear regression models. We discuss its foundations and limitations. It is important to understand this tool thoroughly as many other approaches make use of it. Also many modelling principles that are easily demonstrated on the linear regression translate to more complex models. These principles include: understanding data limitations, exploring variation in variables, collinearity, weighting, interpretation, orthogonality/independence, geometry of linear squares, interaction of variables, model selection, compression, statistical significance and hypothesis testing.

Many of the concepts are illustrated in R using a computer. Some familiarity with R is certainly very useful. Attached you may find an R code that replicates these examples.

Slides from the presentation may be downloaded here. The last slide includes detailed list of potentially useful references.

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