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 alternative econometric 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). Building upon the motivation and the definition of an economic research question, the course will discuss alternative microeconometric methods placing particular attention on the interpretation of their predictions and the discussion of their contribution in terms of identification and estimation of causal effects in order to understand the determinants of household and firm behaviour in the markets.

1. Knowledge and comprehension outcomes:
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. Application of knowledge and comprehension:
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.

Mandatory prerequisites: https://www.unive.it/pag/24741/

Students must be familiar with the contents of the Probability Theory and Econometrics courses of the Master’s Degree Programme in Economics and Finance.

In particular, students should know the following subjects, at least at the introductory level:

- random variables, distribution, expectations, moments;
- joint distributions, marginal distributions, conditional distributions, uncorrelation, independence;
- point and interval estimation;
- estimator properties in finite samples and asymptotic theory;
- hypothesis testing;
- matrix algebra;
- linear regression models and ordinary least squares estimation (OLS);
- maximum likelihood estimation.

Conditional expectations and related concepts in econometrics: partial effects, elasticities and semielasticities;

Linear regression models and ordinary least squares estimation: an overview;

Endogenous regressors and instrumental variables in the linear regression model: motivation, estimation methods and specification tests;

Linear regression models based on 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.

Jeffrey M. Wooldridge. Econometric analysis of cross-section and panel data. Massachusetts Institute of Technology (MIT) Press, 2010.

The exam is written and consists of open questions on theoretical aspects and exercises on the topics included in the programme of the course. The exam is designed to assess the knowledge of the main features of the econometric models discussed in the course, the interpretation of the results produced by these models and the students’ ability of assessing the contribution of these models to address causal inference questions. The exam is closed-notes, closed book.

The course consists of:
a) classes;
b) hands-on sessions led by a professional instructor (tutor) aimed at showing how to apply the models studied in the course to conduct real empirical analyses through statistical-econometric software;
c) individual study.

English

All the teaching material made available to students will be uploaded on the Moodle page of the course.

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