Credit hours
In-class work per week |
Practice per week |
Credits |
Duration |
Total |
6 |
2 |
8 |
10 weeks |
120 hours |
Instructor
Ana Lucia Kassouf
Marcelo Justus dos Santos
Objective
This course aims to equip graduate students with the theoretical and applied tools necessary to understand, specify, and estimate econometric models used in economic analysis. It focuses on multiple linear regression, including parameter estimation, properties of estimators, hypothesis testing, and the use of binary variables. Additionally, the course emphasizes modern methods for causal inference and impact evaluation, enabling students to rigorously assess economic relationships and public policies using techniques such as randomized controlled trials, propensity score matching, regression discontinuity design, instrumental variables, difference-in-differences, and panel data models with fixed effects.
Content
Multiple Linear Regression
• Parameter Estimation
• Properties of Estimators
• Hypothesis Testing
• Binary Variables
Impact Evaluation Methods
• Randomization Method – Randomized Controlled Trials (RCT)
• Matching Methods – Propensity Score Matching
o Maximum Likelihood Estimation
o Probit Models
o Logit Models
• Regression Discontinuity Design (RDD)
• Instrumental Variables (IV)
• Difference-in-Differences Method
• Panel Data Models – Fixed Effects
Bibliography
Angrist, J e J-S Pischke. Mostly Harmless Econometrics. Princeton University Press. 2009.
Cameron, A. Trivedi, P. Microeconometrics, Methods and Applications, Cambridge Press. 2005
Cunningham, S. Causal Inference, The Mixtape. Yale University Press. 2021
Greene, W. H. Econometric Analysis, 8ª edição, Macmillian Publishing Company, 2017.
Stock, J & M Watson. Introduction to Econometrics, Global Edition 4th Edition.2020
Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data. 2nd edition. The MIT Press. 2010.
Wooldridge, J. M. Introdução à Econometria. Uma abordagem Moderna 6ª edição. Thomson Ed. 2017.