A.A. di erogazione 2016/2017
Insegnamento obbligatorio

 (A.A. 2016/2017)


Anno di corso: 
Tipologia di insegnamento: 
Settore disciplinare: 
Secondo Semestre
Ore di attivita' frontale: 
Dettaglio ore: 
Lezione (80 ore)

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.


The objective of the course is to provide the students with some econometric tools for the analysis of the behavior of industries and markets, i.e. the linear regression model, as estimated through OLS or through IV, the simultaneous equations model, and discrete choice models, as estimated through ML.
The course starts with an introduction to the free and open-source software R, that will be used for all applications and examples.

This module deals with the econometrics of industrial organization, both in the case of perfectly competitive markets and in the case of imperfect competition.
We start with the econometric analysis of prices and quantities in competitive markets.
The classical way to analyse the market equilibrium, i.e. the mechanism for which a certain amount of a good is sold at a certain price in a competitive market, is to see it as the intersection of a demand and of a supply curve. Therefore we present econometric tools for the estimation of these functions, namely simultaneous equations models.
After this, we address some weaknesses arising in this way of modelling markets. While in mainstream economic theory the firm has often been considered as a "black box", we will take a look inside the box to describe the productive structure of the firm using the concepts of production frontier and efficiencies. This paves the way for the introduction of oligopoly models, in which firms are no longer price-takers but strategically internalize the demand function of the consumers. We then consider demand-supply models in which expectations and delays create dynamics in prices and quantities. After that, we introduce some models, from economics, management and psychology, allowing for differentiation in the demand and supply of products. The last topic of this module concerns the diffusion of new products; the exposition will benefit from insights from marketing, sociology, organization sciences.

In this module, we study the effects of interventions by firms or external agents.
In the first part, we extend the scope of the first module and consider goods for which markets do not (yet) exist. After introducing discrete choice models, we apply them to the assessment of goods, like environmental or cultural ones, that are not tradable in a market, and to the decisions of firms, especially as concerns the creation of new products and the entry in new markets.
In the second part, we completely change our point of view and we study the impact of policy interventions on economic agents in regulated markets. When the objective is to study the effect of a policy change on the behavior of individuals, we would like to use the estimated relation to simulate the effect of alternative circumstances on the economic reality. In natural sciences, this problem is generally solved through a careful design of the experiment in which data are collected. In social sciences, this is generally impossible as classical controlled experiments are often unfeasible because of economic or ethical constraints. Therefore, we are compelled to use more refined inferential techniques to obtain causal relations from observational data. In this part of the course, we will describe the design of controlled experiments and we will see in which sense economic data do not comply with the standards of this class of experiments. Then we will describe the techniques that can be used to infer causal relationships in the absence of experimental data.

Mode of Delivery: The course will be composed of 80 hours of classical lectures.
Assessment: Students will be evaluated on the basis of an essay discussing a topic relevant to the course. A written exam will be introduced if necessary.

The course will be based on a set of slides written by the lecturer, covering both theory and specialized paper-length examples. A list of academic papers or blog posts addressing more advanced topics will be provided to the students as additional readings. Mathematical derivations will be dealt with in handouts written by the lecturer. Solved exercises will also be provided.

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