Universidad Carlos III de Madrid
Area of Study
Business, Business Administration, Economics, International Business, International Economics
Taught In English
STUDENTS ARE EXPECTED TO HAVE COMPLETED Mathematics for Economics I and II, Statistics I and II, Econometrics, Econometric Techniques, Quantitative Economics.
Course Level Recommendations
ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.
Recommended U.S. Semester Credits3
Recommended U.S. Quarter Units4
Hours & Credits
OverviewCOMPETENCES AND SKILLS THAT WILL BE ACQUIRED AND LEARNING RESULTS.This course is designed for students interested in pursuing graduate studies. The course offers an advancedtreatment of econometric techniques already discussed in previous courses. Asymptotic theory is the basic tool forjustifying statistical inferences on a variety of models under different information on the data generating process.The statistical foundations of classical estimation and testing principles are discussed in the context of reduced andstructural form models under nonstandard conditions, which include data exhibiting unknown serial dependence.We pay attention to necessary and sufficient restrictions for structural parameters identification which is essentialfor consistency. Numerical optimization techniques applied in the computation of extreme estimators arereviewed. Data management and programming in R is an important learning tool in this course.At the end of the course, the students should be able to study research articles in professional economics journals.To this end, the student will get a good working knowledge of basic asymptotic theory concepts applied tostatistical inferences and will be equipped with programming skills useful for implementing econometrics proposals.DESCRIPTION OF CONTENTS: PROGRAMME1. Causal relations and partial effects: Causal relations and ceteris-paribus analysis. Conditionalexpectations, linear projections and partial effects. Elasticities and semi-elasticities. Linear andnonlinear parametric models for causal relations.2. Basic asymptotic theory: Convergence in probability and distribution. Law of large numbers andcentral limit theorems. The analog principle. Asymptotic behaviour of estimators and test statistics. Thedelta-method.3. Least squares estimation in the single-equation linear model: Asymptotic properties of ordinaryand generalized least squares under standard conditions. Trending regressors. Quasi-maximumlikelihood estimation under Gaussianity. Ignoring omitted variables, proxy variables and measurementerrors. Estimating the asymptotic variance in the presence of heteroskedasticity and serial dependenceof unknown form.4. Testing parameter restrictions in the single-equation linear model: Linear restrictions onparameters. Restricted least squares. Consistency, asymptotic power and efficiency of tests. Wald,Lagrange Multiplier and Likelihood Ratio. Heteroskedasticity and residual lack of autocorrelation tests.5. Instrumental variable estimation in the single-equation linear model: Reduced versus structuralforms. The identification problem. Instrumental variables and 2SLS. Asymptotic inferences using 2SLS.Tests of endogeneity and overidentifying restrictions.Página 1 de 26. Estimating systems of linear equations: OLS and GLS in seemingly unrelated regression systems.Identification in systems of structural equations under general linear restrictions. Asymptotic inferencebased on 2SLS versus 3SLS. Identification under cross-equations and covariance restrictions. Nonlinearin variables models.7. Extremum estimators: Asymptotic properties of extremum estimators. Numerical optimizationalgorithms. The quasi-maximum conditional likelihood estimator. Application to some limiteddependent variable models.LEARNING ACTIVITIES AND METHODOLOGYThe homeworks are used to guide the study of the subject. Each week the student has to apply results andtechniques discussed in the lectures. The course is of a methodological nature and does not require the use ofcomputers.ASSESSMENT SYSTEM(Final Exam)/2+(Mid Term Exam)/4+Homework/4% end-of-term-examination: 50% of continuous assessment (assigments, laboratory, practicals?): 50BASIC 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., 2002ADITIONAL BIBLIOGRAPHY- T. Amemiya Advanced Econometrics, Harvard University Press, Cambridge, MA., 1985- C. Gourieroux and A. Monfort Statistics and Econometric Models, Vol. 1 and 2, Cambridge University Press,Cambridge, U.K., 1995- W. Greene Econometric Analysis, Pearson -Prentice Hill, Upper Daddle River, N.J., 1997- J. Johnson and J. Dinardo Econometric Methods, MacGraw-Hill, New York. N.J., 1997- R.C. Mittelhammer, G.G. Judge and D.J. Miller Econometrics Foundations, Cambridge University Press,Cambridge, U.K., 2000- P. Ruud An introduction to Classical Econometric Theory, Oxford University Press, Oxford, U.K., 2000
Please note that there are no beginning level Spanish courses offered in this program.
Courses and course hours of instruction are subject to change.
Eligibility for courses may be subject to a placement exam and/or pre-requisites.
Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.
ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.
Please reference fall and spring course lists as not all courses are taught during both semesters.
Availability of courses is based on enrollment numbers. All students should seek pre-approval for alternate courses in the event of last minute class cancellations
Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.