Econometrics Methods

Code

200090

Academic unit

NOVA Information Management School

Credits

7.5

Teacher in charge

José António de Almeida Pinheiro

Teaching language

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

Objectives

Students will develop knowledge of estimation methods for regression models. Emphasis will be placed on interpreting correctly the statistical results when applied to empirical problems. Students will also have the opportunity to develop competences with econometric software.

Prerequisites

Statistics

Subject matter

Program
1. Ordinary least squares and fundamental statistical inference concepts (6 classes, K3 [Kennedy Ch. 3], K4, K6, K7, K8, K9, K10, K11)
2. Panel data methods (4 classes, K17)
(a) Fixed effects model
(b) Random effects model
3. Causal inference (4 classes, C)
(a) Difference-in-differences
(b) Matching models

Bibliography

K Kennedy, Peter, ¿A Guide to Econometrics¿, 5th Edition, Blacwell Publishing.; W Wooldridge, Jeffrey M., ¿Introductory Econometrics¿, Thomson South Western.; C Own classnotes; 0; 0

Teaching method

Theoretical classes and practical exercises using an econometric software, e.g. Gretl

Evaluation method

Final exam, 100%.

Courses

• PostGraduate in Information Analysis and Management
• PostGraduate Information Systems and Technologies Management
• PostGraduate in Knowledge Management and Business Intelligence
• PostGraduate in Information Systems Governance
• PostGraduate in Marketing Research e CRM
• PostGraduate in Intelligence Management and Security
• PostGraduate in Marketing Intelligence
• PostGraduate in Enterprise Information Systems
• PostGraduate in Digital Enterprise Management
• PostGraduate Marketing Research e CRM
• PostGraduate in Information Systems and Technologies Management
• PostGraduate Risk Analysis and Management
• PostGraduate in Smart Cities
• PostGraduate in Information Management and Business Intelligence in Healthcare
• PostGraduate Digital Marketing and Analytics