Currículo

Econometrics I ECON1

Contextos

ECTS

6.0 (para cálculo da média)

Objectivos

1. Develop knowledge about estimation and inference using the ordinary least squares (OLS) method in the context of modelling cross-sectional data. 2. Understand the assumptions that make OLS estimation valid and its limitations. Be able to test the specified assumptions and know alternative robust estimation procedures. 3. Learn estimation and inference methods for regressions with binary dependent variables. 4. Develop familiarity with empirical applications to real data using appropriate software, focusing on result interpretation and validity

Programa

1. Introduction: Conditional distributions Introduction to econometric software (R, Stata, or Python) with exploratory data analysis 2. The Nature of Econometrics and Economic Data 3. Multiple Linear Regression Model: Estimation 4. Multiple Linear Regression Model: Inference 5. Multiple Linear Regression Model: Asymptotic Properties 6. Multiple Linear Regression Model: Additional Problems on Functional Form 7. Heteroskedasticity 8. Multiple Linear Regression Model with Qualitative Information: Binary or Dummy Variables 9. Binary Choice Models

Método de Avaliação

The teaching regime is based on a division into theoretical classes (2 classes of 1h30 per week) and practical classes (1 class of 2 hours per week). The theoretical classes cover the material using empirical economic examples as illustration. The practical classes focus on solving exercises taken from the manual listed in the main bibliography of the course and adapted questions from previous exams. Evaluation will be based on written exams according to ISEG's RGAC. With support from teaching assistants, there is the possibility of weighting the grade of the Normal Period exam with the assessment resulting from mandatory group work carried out throughout the semester involving empirical software use.

Carga Horária

Bibliografia

Não foi definida bibliografia principal

Disciplinas de Execução

2025/2026 - 2 Semestre