Advanced Econometrics

Universidad Carlos III de Madrid

Course Description

  • Course Name

    Advanced Econometrics

  • Host University

    Universidad Carlos III de Madrid

  • Location

    Madrid, Spain

  • Area of Study

    Business Administration, Economics, International Business, International Economics

  • Language Level

    Taught In English

  • Prerequisites

    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.

    Hours & Credits

  • ECTS Credits

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    This course is designed for students interested in pursuing graduate studies. The course offers an advanced
    treatment of econometric techniques already discussed in previous courses. Asymptotic theory is the basic tool for
    justifying 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 and
    structural 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 essential
    for consistency. Numerical optimization techniques applied in the computation of extreme estimators are
    reviewed. 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 to
    statistical inferences and will be equipped with programming skills useful for implementing econometrics proposals.
    1. Causal relations and partial effects: Causal relations and ceteris-paribus analysis. Conditional
    expectations, linear projections and partial effects. Elasticities and semi-elasticities. Linear and
    nonlinear parametric models for causal relations.
    2. Basic asymptotic theory: Convergence in probability and distribution. Law of large numbers and
    central limit theorems. The analog principle. Asymptotic behaviour of estimators and test statistics. The
    3. Least squares estimation in the single-equation linear model: Asymptotic properties of ordinary
    and generalized least squares under standard conditions. Trending regressors. Quasi-maximum
    likelihood estimation under Gaussianity. Ignoring omitted variables, proxy variables and measurement
    errors. Estimating the asymptotic variance in the presence of heteroskedasticity and serial dependence
    of unknown form.
    4. Testing parameter restrictions in the single-equation linear model: Linear restrictions on
    parameters. 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 structural
    forms. The identification problem. Instrumental variables and 2SLS. Asymptotic inferences using 2SLS.
    Tests of endogeneity and overidentifying restrictions.
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    6. Estimating systems of linear equations: OLS and GLS in seemingly unrelated regression systems.
    Identification in systems of structural equations under general linear restrictions. Asymptotic inference
    based on 2SLS versus 3SLS. Identification under cross-equations and covariance restrictions. Nonlinear
    in variables models.
    7. Extremum estimators: Asymptotic properties of extremum estimators. Numerical optimization
    algorithms. The quasi-maximum conditional likelihood estimator. Application to some limited
    dependent variable models.
    The homeworks 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
    (Final Exam)/2+(Mid Term Exam)/4+Homework/4
    % end-of-term-examination: 50
    % of continuous assessment (assigments, laboratory, practicals?): 50
    - 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
    - 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

Course Disclaimer

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.


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