Checking date: 22/05/2018

Course: 2018/2019

Topics in advanced Econometrics
Study: Bachelor in Economics (202)

Coordinating teacher: VELASCO GOMEZ, CARLOS

Department assigned to the subject: Department of Economics

Type: Electives
ECTS Credits: 6.0 ECTS


Students are expected to have completed
This course is designed for students with a strong background in econometrics and statistics acquired in previous courses: Mathematics for Economics I and II, Statistics I and II, Econometrics, Econometric Techniques and Quantitative Economics.
Competences and skills that will be acquired and learning results. Further information on this link
This is an advanced course in econometrics which builds upon previous B.Sc. courses in econometrics (Econometrics, Econometric Techniques and Quantitative Economics.) The focus will be on theoretical foundations of econometrics, including the asymptotic theory behind inferences based on ordinary least squares (OLS), maximum likelihood (ML) and generalized method of moments (GMM). Single and multiple equation models are covered.
Description of contents: programme
1. Finite sample properties of ordinary least squares (OLS): The classical regression model. The algebra of least squares. Finite sample properties of OLS. Hypothesis testing under normality. Relation to maximum likelihood. Generalized least squares. 2. Large sample theory: Review of limit theorems for sequences of random variables. Fundamental concepts in time-series analysis. Large-sample distribution of the OLS estimator. Hypothesis testing. Consistent estimation of the asymptotic variance of OLS estimators. Implications of conditional homoscedasticity. Testing conditional homoscedasticity. Least squares projection. Consistent estimates of projection coefficients. Testing for lack of autocorrelation. 3. Single-equation generalized method of moments (GMM): Endogeneity bias. The general formulation. Generalized method of moments defined. Large sample properties of GMM. Testing overidentified restrictions. Hypothesis testing by likelihood-ratio principle. Implications of conditional homoscedasticity. 4. Multiple-equations GMM: The multiple-equations model. Multiple-equation GMM defined. Large sample theory. Single-equation versus multiple-equations estimation. Special cases of multiple equations GMM: FIVE, 3SLS and SUR. Common coefficients.
Learning activities and methodology
Assignments are used to guide the study of the subject. Each week the student has to apply results and techniques discussed in the lectures. The course is of a methodological nature and does not require the use of computers.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Basic Bibliography
  • Hayashi, F. . Econometrics. Princeton University Press, Princeton, N.J.. 2000
  • J.W. Wooldridge. Econometric Analysis of Cross-Section and Panel Data. The MIT Press, Cambridge, MA.. 2002
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • C. Gourieroux and A. Monfort. Statistics and Econometric Models, Vol. 1 and 2. Cambridge University Press, Cambridge, U.K.. 1995
  • J. Johnson and J. Dinardo. Econometric Methods. MacGraw-Hill, New York. N.J.. 1997
  • J. Shao. Mathematical Statistics. Springer. 2003
  • P. Ruud. An introduction to Classical Econometric Theory. Oxford University Press, Oxford, U.K.. 2000
  • R.C. Mittelhammer, G.G. Judge and D.J. Miller. Econometrics Foundations. Cambridge University Press, Cambridge, U.K.. 2000
  • T. Amemiya . Advanced Econometrics. Harvard University Press, Cambridge, MA.. 1985
  • W. Greene . Econometric Analysis. Pearson -Prentice Hill, Upper Daddle River, N.J.. 1997
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

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