Checking date: 21/03/2019


Course: 2019/2020

Econometrics
(13647)
Study: Bachelor in Economics (202)


Coordinating teacher: DELGADO GONZALEZ, MIGUEL ANGEL

Department assigned to the subject: Department of Economics

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Students are expected to have completed
Mathematics for Economics I Mathematics for Economics II Statistics I Statistics II Principles of Economics Microeconomics
Competences and skills that will be acquired and learning results. Further information on this link
This course offers an introduction to data analysis in Social Science with the assistance of the multiple regression model. The emphasis is on the interpretation of the model and the application of statistical inference techniques to solve relevant practical problems. The course discusses in detail how to make inferences under non-standard situations, relevant in Social Sciences, due to the nature of the variables in the model (qualitative, transformed to allow nonlinear relations or non-observable,) or to the nature of data. The rigorous formal justification of the applied statistical inference techniques is out of the scope of this course. The background in Probability, Statistics, Algebra and Calculus offered in Mathematics I & II and Statistics I & II is more than enough for this course. At the end of the course, the student will acquire a good working knowledge on the interpretation of the linear regression model, discriminating between alternative specifications by means of statistical inference, and using GRETL for estimation and hypothesis testing.
Description of contents: programme
This course offers an introduction to data analysis in Social Science with the assistance of the multiple regression model. The emphasis is on the interpretation of the model and the application of statistical inference techniques with the objective of solving relevant practical problems. The course discusses in detail how to make inferences under non-standard situations, relevant in Social Sciences, due to the nature of the variables in the model (qualitative, transformed to allow nonlinear relations or non-observable) or to the nature of data. The course follow Chapters 4 to 8 of Stock & Watson (2012). Syllabus: 1. The nature of econometrics and economic data (SW. Ch. 1, 2 & 3) 2. The simple regression model (SW. Ch. 4,5). 3. Multiple regression analysis: estimation (SW. Ch. 6) 4. Multiple regression analysis: inference (SW. Ch. 7) 5. Nonlinear regression using linerar multiple regression (SW. Ch. 8). 6. Discrete choice (SW. Cp. 11). 7. Instrumental variables estimation and two stages least squares (SW. Cp. 12).
Learning activities and methodology
The free software GRETL is the main learning tool of this course. The different concepts are discussed in the context of analyzing relevant cases of study in Social Sciences using real data. Homeworks, to be handed in class periodically, require to use GRETL.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Basic Bibliography
  • Goldberger, A.S.. Introductory Econometrics. Harvard University Press. 1998
  • Greene, W.H.. Econometric analysis . Prentice Hall. 2008
  • Gujarati, D.N.. Basic Econometrics. McGraw-Hill. 2009
  • Jonhston, J.. Econometric Methods. The McGraw-Hill Companies. 1997
  • Stock, J.H. & M.W. Watson. Introduction to Econometrics. Addison Wesley. 2012
  • Wooldridge, J.M.. Introductory Econometrics. A Modern Approach. South-Western College Publishing. 2009
Additional Bibliography
  • Hayashi, F.. Econometrics. Princeton University Press. 2000
  • Wooldridge, J.M.. Econometric analysis of cross section and panel data . The MIT Press. 2009

The course syllabus and the academic weekly planning may change due academic events or other reasons.


More information: http://www.eco.uc3m.es/docencia/econometria/index.html