EMPIRICAL ECONOMICS

This course is offered among the elective courses of the Master’s Degree Programme in Economics and Finance and is aimed at providing advanced tools for the comprehension and the development of applied microeconometric analyses. The course will propose econometric and quantitative methods for the analysis of cross-sectional data (for instance, samples of households or firms in a given year) or panel data (for instance, samples of households or firms followed over time). The course will discuss alternative microeconometric methods placing particular attention on the interpretation of their predictions and the discussion of the policy implications.
The course intends to emphasize the practical application approach rathre than the theoretical approach

1. Knowledge and understanding:
1.1. Knowing microeconometric models for the analysis of cross-section and panel data;
1.2. Knowing how to present and interpret the results produced by these models to answer a research question in economics;
1.3. Knowing the hypotheses at the basis of the models studied during the course and their consequences on the identification and the estimation of causal effects in economic research questions.

2. Applying knowledge and understanding:
2.1. Applying the models studied in the course to answer a research question in economics;
2.2. Comparing the results produced by alternative econometric models based on the different hypotheses on the data generating process and their reliability in the research questions at hand;
2.3. Applying the economic theory to assess the contribution of alternative microeconometric models in terms of identification and estimation of causal effects


3. Evaluation and project skills:
3.1. Designing empirical analyses that exploit cross-sectional or panel data to address an economic research question of interest;
3.2. Evaluating the contribution provided by papers in the scientific literature that exploit microeconometric models to address an economic research question of interest;
3.3. Thinking about the design of microeconometric models aimed at overcoming issues in the identification and estimation of causal effects.


4. Lifelong learning skills
4.1) Master complex quantitatve problems.
4.2) Master assumptions of economic problems to be implemented in applications
4.3) Ability to make use of new tools and adapt competences.

Mandatory prerequisites: https://www.unive.it/pag/24741/
Students must be familiar with the contents of the Probability Theory and Basic Econometrics courses of the Master’s Degree Programme in Economics and Finance.
In particular, students should be familiar with the following subjects, at least at the introductory level:
- random variables, distribution, expectations, moments;
- joint distributions, marginal distributions, conditional distributions
- hypothesis testing;
- linear regression models and ordinary least squares estimation (OLS);

Conditional expectations and distributions
Linear regression models and ordinary least squares estimation: an overview;
How to solve the "endogeneity problem": instrumental variables in the linear regression model: motivation, estimation methods and specification tests;
Using panel data: advantages of panel data, pooled ordinary least squares, random effects model, fixed effects model, first differencing methods;
Models with binary dependent variables for cross-sections and panel data;
Sample selection bias: the Heckman model.
How to build a simulation and make it work.

The course is based on case studies: a presentation of the lectures will be provided on slides at the dedicated moodle platform, however elements of the course content are also in:
Jeffrey M. Wooldridge. Econometric analysis of cross-section and panel data. Massachusetts Institute of Technology (MIT) Press, 2010.

Grading is based on the work done during the years (homeworks and study groups) and on a final written exam.

The course is strongly based on practical experience and case studies, flipped classes. Lectures will support the organization of the material

English

Grading is based on the work done during the years (homeworks and study groups) and on a final written exam

written

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development