C1 - Quantitative methods: econometrics
- Statistics - Rosaria Ignaccolo (University of Turin)
- Basic fundamentals in econometrics - Luigi Benfratello (Politecnico di Torino)
- Stata - Fabio Berton (University of Turin)
C1 - Quantitative methods: econometrics and impact evaluation
- Applied econometrics - Luigi Benfratello (Politecnico di Torino)
- Impact evaluation - Lia Pacelli (University of Turin, Laboratorio R: Revelli)
C1 - Microsimulation:
- General equilibrium models - Mohamed Ali Marouani (University of Paris I, IRD-DIAL and ERF)
- Micro-simulation - Roberto Leombruni (University of Turin, Laboratorio R. Revelli)
The course will offer an overview of some standard topics in probability and inferential statistics. Students will be introduced to probabilistic and statistical tools that are routinely used in Econometrics and Economics:
- Review of some probability concepts: discrete and continuous random variables;
- Introduction to inferential statistics: sampling procedure and sample distributions;
- Point Estimation, interval estimation, hypothesis testing.
Basic fundamentals in ecometrics
The course aims at providing students with the knowledge of basic concepts and tools in econometrics. The course will focus on linear regression models with both single and multiple regressors. Topics will be estimation by Ordinary Least Squares, interpretation of coefficients according to the nature of regressors (continuous or discrete) and to the functional form of the regression function, as well as inference in finite sample and asymptotically. The classes are complemented by lab session where real data are analysed with the help of the Stata econometric software.
This course aims at introducing MALED students to the use of the statistical software STATA, and to make them independent STATA learners. In addition, the course will also apply some of the theory presented in the Statistics course. Covered topics include:
- Creation and use of data bases;
- Creation and manipulation of variables;
- Tabular and graphical description of variables;
- Creation and manipulation of random variables;
- Hypothesis testing.
The course is intended to provide students with knowledge of the basic tools in econometrics. The topics covered include estimation and inference in linear regression (with single and multiple regressors), panel data, and limited (binary, censored and truncated) dependent variable models. The classes are complemented by lab session where real data are analysed with the help of the Stata econometric software.
The course will deal with:
- Conceptual issues, which will focus on causal effect, definition of outcome, counterfactual and controls;
- Managing impact evaluation, in particular data collection, planning, policy test and roll-out.
The methods discussed and formalized will be:
- randomized experiments;
- diff in diffs;
- instrumental variables;
- regression discontinuity design.
The classes will be complemented by lab sessions where real data are analysed with the help of the Stata econometric software.
General equilibrium models
The course will deal with applied general equilibrium models (AGE) with a focus on labor markets issues and mainly on how to model some stylized facts observed in developing countries’ labor markets within AGE frameworks.
We will focus on:
- Introduction to applied general equilibrium modeling;
- The labor demand block;
- Wage setting mechanisms and unemployment;
- Labor supply, informal labor and migration;
- Data issues.
The object of the course is the use of micro-simulations for the study of labour markets and for labour market policy design and evaluation.
The course will firstly focus on the basic ingredients of a microsimulation:
- The sinthetic population - which has to be representative of the target population;
- The normative rules – which are to be a detailed copy of actual ones and of the new ones object of the study;
- The individual behaviours - which have to be implemented according to some realistic behavioural model.
The second step of the course will be to show how these ingredients can be used to build static vs. dynamic simulations, and accounting vs. behavioural simulations.
The third step of the course will be to discuss the inferential aspects of simulation models and review the main software packages that can be use to implement professional microsimulations.