Econometrics Methods

Objectives

1. Understanding the context for simple linear regression.
2. How to evaluate simple linear regression models
3. How a simple linear regression model is used to estimate and predict likely values
4. Understanding the assumptions that need to be met for a simple linear regression model to be valid
5. How multiple predictors can be included into a regression model
6. Understanding the assumptions that need to be met when multiple predictors are included in the regression model for the model to be valid
7. How a multiple linear regression model is used to estimate and predict likely values
8. Understanding how categorical predictors can be included into a regression model
9. How to transform data to deal with problems identified in the regression model
10. Alternative methods for estimating a regression line besides using ordinary least squares
11. Understanding regression models in time dependent contexts
12. Understanding regression models in non-linear contexts

General characterization

Code

200090

Credits

7.5

Responsible teacher

Manuel José Vilares

Hours

Weekly - Available soon

Total - Available soon

Teaching language

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

Prerequisites

Bibliography

- Kutner, M. H., Nachtsheim, C., Neter, J., Li, W. (20 05). Applied linear statistical models. 5th edition, McGraw-Hill/Irwin
- Wooldridge, J. M. (2009). Introductory Econometrics. A Modern approach. 4th Edition, South Western
- Greene, W. H. (2012). Econometric Analysis. 7th Edition. Prentice Hall
- Johnston, J. , Dinardo, J (1997). Econometrics Methods. 4th Edition. Economics Series, McGraw Hill

Teaching method

The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications (with and without software), questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component is geared towards solving problems and exercises, including discussion and interpretation of results. A set of exercises to be completed independently in extra-classroom context is also proposed.

Evaluation method

Evaluation:

1st call: project (40%), first round exam (60%)

2nd call: final exam (100%)

Subject matter

1. Simple Linear Regression (SLR)
2. SLR Model Evaluation
3. SLR Estimation & Prediction
4. SLR Model Assumptions
5. Multiple Linear Regression (MLR)
6. MLR Model Evaluation
7. MLR Estimation, Prediction & Model Assumptions
8. Categorical Predictors and Data transformations
9. Model Building
10. Influential Points
11. Multicollinearity & Other Regression Pitfalls
12. Weighted Least Squares & Robust Regression
13. Time Series & Autocorrelation
14. Logistic, Poisson & Nonlinear Regression

Programs

Programs where the course is taught: