Econometrics

Recommended Prerequisites

Calculus I, Calculus II, Linear Algebra, Applied Economics, Statistics.

Teaching Methods

Theoretical lectures: in each lecture students are informed about the subjects to be taught in the following lecture. This information is available at nonio, with theoretical texts. Theoretical lectures consist mainly on the oral presentation of subjects, frequently motivated by introductory questions and practical examples. The oral presentation is usually accompanied by data show projection and use of econometric software.

Applied lectures: solving of exercises available to students at the beginning of the semester. Occasional use of an econometric software.

Learning Outcomes

Overall objectives and generic competencies

General understanding of the notion of regression model, as an approximation to the conditional mean of an economic variable, given a set of explanatory variables. Knowledge of the assumptions which allow the use of the OLS estimation method.

Specific objectives and competencies

Understanding the notion of regression model, as an approximation to the conditional mean of an economic variable, given explanatory variables. Correctl use of the OLS method to estimate economic relations and test economic theories. Understanding and ability to recognize the conditions under which this method can be used, as well as the statistical properties of its related estimators and testing procedures. Understanding the basic aspects of the regression analysis with both cross-section and time series data. Ability to solve simple theoretical/applied and applied exercises; ability to apply the above techniques in simple macro- and microeconomic contexts.

Work Placement(s)

No

Syllabus

Joint distributions and conditional expectation (revision).

1.  Nature of Econometrics and economic data.

2.  Regression analysis of cross-sectional data.
2.1 Simple regression model.
2.2 Multiple regression model.
2.2.1 Estimation.
2.2.2 OLS: statistical inference.
2.2.3 OLS: asymptotic properties.
2.3 Regression analysis with qualitative information: dummy variables.
2.4 Heteroskedasticity.

3.  Regression analysis of time series data.
3.1 Basic issues.
3.2 Additional aspects of the regression analysis of time series data.
3.3 Autocorrelation and heteroskedasticity in models for time series data.

Head Lecturer(s)

José Maria Ruas Murteira

Assessment Methods

Assessment
Exam or Two tests. Final grade: average of grades in both tests. Minimum grade per test: 6/20.: 100.0%

Bibliography

WOOLDRIDGE, Jeffrey M. - Introductory econometrics : a modern approach. 4th ed.. [South Melbourne?]: South-Western/Cengage Learning, 2009. [BP 519.8 WOO]