# Econometrics I

## Objectives

- 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.

## General characterization

##### Code

100049

##### Credits

6.0

##### Responsible teacher

Carolina Micael de Abreu e Vasconcelos

##### Hours

Weekly - Available soon

Total - Available soon

##### Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

### Prerequisites

Algebra matrix and statistics (recommended)

### Bibliography

- Hill, R. C., Griffiths, W. E. e Lim, G. C. (2012). Principles of Econometrics. 4th edition, John Wiley and Sons;
- Wooldridge, J.M. (2013): Introductory Econometrics: A Modern Approach, 5th Edition, South Western Cengage Learning;
- Oliveira, M. et al. (2011). Econometria. Escolar Editora; Greene, W. H. (2012). Econometric Analysis. 7th Edition. Prentice Hall;
- Gujarati, D., Porter, D. (2009). Basic Econometrics. 5th Edition. McGraw-Hill/Irwin.

### Teaching method

- 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.

### Evaluation method

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.

## Subject matter

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