In the last one or two decades, the classical way of teaching economics, through theoretical models and without empirical evidence, has been questioned. The prominence of this framework is caused by the fact that introductory courses are generally taught when students do not have yet the statistical skills that would allow them to appraise critically the data. The solution that has been advocated very recently is to teach theory together with data analysis.
This course is structured exactly as an interplay between economic theory and econometric data analysis. Each economic topic is presented together with the econometric tool that can be used to obtain estimates connected with it. When topics accumulate, more and more applications of the same econometrics tools will be introduced.
An additional feature of the course is that the presentation of the econometric methods follows roughly their historical development. First of all, the linear regression that was introduced around 1885 by Francis Galton will be presented. The consumer and producer problems were formalized around the same time by the marginalist school. The general equilibrium theory in economics and simultaneous equations estimation in econometrics are the main contributions of the Cowles Commission, founded in 1932. Both these topics converge in the estimation of demand-supply equilibria.
Then we tackle some topics that have been developed between the 30’s and the 90’s, and that account for some deviations from the classical description of producers, consumers and market equilibria. Then we turn to discrete choice models and their applications, a topic developed in the 70’s and 80’s, and that has led to a better comprehension of individual behavior. This establishes a link with behavioral and experimental economics.
At last, we consider the models and methods developed in in the so-called "empirical revolution”, a movement in economics that has allowed researchers to study economic phenomena from the empirical point of view with much greater efficiency and effectiveness than in the past.
The most trivial competence that the student should obtain from the course is to master the interpretation of the results of OLS, IV, 2SLS and ML estimation. More ambitiously, the course aims at teaching something about the scientific method, namely how statistical inference can help in testing hypotheses and which arrangements have to be done to this procedure in a complex context such as the economic environment.