Análise de Regressão
Objetivos
- Formulate and specify econometric models to interpret economic phenomena for sectional data;
- Recognize and understand completely the multiple linear regression model used in cross-sectional.
Caracterização geral
Código
100164
Créditos
6.0
Professor responsável
Carolina Maria de Abreu Braziel Shaul
Horas
Semanais - A disponibilizar brevemente
Totais - A disponibilizar brevemente
Idioma de ensino
Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês
Pré-requisitos
Algebra matrix and statistics (recommended)
Bibliografia
Método de ensino
- The theoretical classes aim to provide the student with the theoretical background for each topic.
- The practical classes aim to apply the concepts and methodologies learned in the theoretical classes.
Método de avaliação
The final grade depends on the continuous assessment (CA) throughout the semester and the final exam (FE) (first and second season).
FG = max{0.4CA + 0.6FE; FE}
The formula above implies the following: if the final exam grade is higher than the continuous assessment grade, the final grade is given exclusively by the final exam grade. If not, the final grade is given by the formula 0.4CA + 0.6FE.
The continuous assessment has two components: the mid-term test and the project.
Conteúdo
1. Introduction and the Natures of Econometrics
- What is Econometrics?
- Steps in Empirical Economic Analysis
- The Structure of Economic Data
- Causality and Ceteris Paribus
2. The Simple Linear Regression Model
- Introduction and Motivation
- Deriving the OLS
- Algebraic Properties of OLS Statistics
- Units of Measurement and Functional Form
- Expected Values and Variances of the OLS Estimators
3. The Multiple Linear Regression Model
- Introduction and Motivation
- Mechanics and Interpretation of OLS
- Finite Sample Assumptions
- Finite Sample Properties
- Inference
4. Heteroskedasticity
- Consequences of Heteroskedasticity
- Testing for Heteroskedasticity
- Weighted Least Squares Estimation
- Heteroskedasticity-robust inference
5. Further Issues
- Asymptotic Properties of the OLS
- Models with Quadratics and Interactions
- Functional Form Misspecification
- Prediction
- Maximum Likelihood Estimation
6. Multiple Regression Analysis with Qualitative Information
- Describing Qualitative Information
- A Single Dummy Independent Variable
- Using Dummy Variables for Multiple Categories
- Interactions Involving Dummy Variables
- The Chow Test