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Graduate econometrics III

academic year
The health crisis linked to the spread of Covid-19 constitutes grounds of force majeure constraining the University to adapt the assessment methods for some of the course units; Information on the new terms of these assessments will be provided to students through the usual information channels by December 4, 2020.

Course teacher(s)

David PREINERSTORFER (Coordinator)

ECTS credits


Language(s) of instruction


Course content

Special interests of participating students can be taken into consideration (if they broadly fit into the framework below); suggestions are welcome and will be discussed in the first lecture.

To give an example, the topics of the lecture could contain (the first item constituting the core component, other topics will be selected in the first lecture):

  1. Revision of hypothesis testing, confidence interval construction, and related optimality concepts.

  2. (Non-) Testability of hypotheses (Bahadur-Savage-type results)

  3. Identification in econometrics.

  4. Impossibility results when parameters can be nearly non-identified (applications to testing under weak instruments, unit root testing, spectral density estimation, and testing for long-range-dependence).

  5. Uniform vs. non-uniform asymptotic approximations with a special emphasis on autocorrelation robust testing, model selection, and the zero power trap in testing for spatial or temporal autocorrelation.

  6. Impossibility results in high-dimensional testing problems.

Objectives (and/or specific learning outcomes)

Successful completion of this course should enable students to understand parts of the current literature on non-standard inference problems in econometrics and to make their own contributions to this literature. Depending on the topics covered, students understand, e.g., the consequences of identification failure for econometric inference, the problems associated with asymptotic results that fail to be uniform in the parameters, or difficulties arising in high-dimensional testing problems. Students know how to conduct Monte Carlo studies to evaluate properties of tests and confidence intervals, and are aware of the practical consequences of the topics discussed.


Required knowledge and skills

An equivalent of the content of ECON-S-428 (Graduate Econometrics I)

Teaching methods and learning activities

The course consists of (i) lectures in which the topics are introduced by the instructor and (ii) lectures where students discuss/present their projects.

Contribution to the teaching profile

This course introduces participants to selected advanced topics in econometrics. The first part of the course develops necessary terminology concerning hypothesis testing, confidence intervals, and related optimality concepts used in econometrics. Then, depending on the interests of the participants, we discuss topics such as the ones mentioned in `` Contents of the teaching unit'' further below.

References, bibliography, and recommended reading

A selective list:

Andrews, D. W., M. J. Moreira, and J. H. Stock (2007). Performance of conditional Wald tests in IV regression with weak instruments. Journal of Econometrics 139 (1), 116-132.

Andrews, I. (2016). Conditional linear combination tests for weakly identi_ed models. Econometrica 84 (6), 2155-2182.

Andrews, I. and A. Mikusheva (2016). Conditional inference with a functional nuisance parameter. Econometrica 84 (4), 1571-1612.

Bahadur, R. R., & Savage, L. J. (1956). The nonexistence of certain statistical procedures in nonparametric problems. Annals of Mathematical Statistics, 27(4), 1115-1122.

Berk, R., Brown, L., Buja, A., Zhang, K., & Zhao, L. (2013). Valid post-selection inference. Annals of Statistics, 41(2), 802-837.

Den Haan, W. J., & Levin, A. T. (1997). 12 A practitioner's guide to robust covariance matrix estimation. Handbook of Statistics, 15, 299-342.

Dufour, J. M. (1997). Some impossibility theorems in econometrics with applications to structural and dynamic models. Econometrica, 1365-1387.

Elliott, G., U. K. Müller, and M. W. Watson (2015). Nearly optimal tests when a nuisance parameter is present under the null hypothesis. Econometrica 83 (2), 771-811.

Imbens, G. W., & Manski, C. F. (2004). Confidence intervals for partially identified parameters. Econometrica, 72(6), 1845-1857.

Kiefer, N. M., Vogelsang, T. J., & Bunzel, H. (2000). Simple robust testing of regression hypotheses. Econometrica, 68(3), 695-714.

Leeb, H., & Pötscher, B. M. (2005). Model selection and inference: Facts and fiction. Econometric Theory, 21(01), 21-59.

Lehmann, E. L., & Romano, J. P. (2006). Testing statistical hypotheses. Springer.

Pötscher, B. M. (2002). Lower risk bounds and properties of confidence sets for ill‐posed estimation problems with applications to spectral density and persistence estimation, unit roots, and estimation of long memory parameters. Econometrica, 70(3), 1035-1065.

Preinerstorfer, D., & Pötscher, B. M. (2016). On size and power of heteroskedasticity and autocorrelation robust tests. Econometric Theory, 32(02), 261-358.

Preinerstorfer, D., & Pötscher, B. M. (2017) On the power of invariant tests for hypotheses on a covariance matrix. Econometric Theory, 33(1).

Romano, J. P. (2004). On non‐parametric testing, the uniform behavior of the t‐test, and related problems. Scandinavian Journal of Statistics, 31(4), 567-584.

Course notes

  • Université virtuelle
  • Podcast

Other information


David Preinerstorfer

ECARES, Université libre de Bruxelles

Office: 42 Ave. F.D. Roosevelt, R42.5.223

E-Mail: david.preinerstorfer(at)ulb.ac.be

Phone: +32 2 650 3366




Method(s) of evaluation

  • Other

Grading is based on (i) written assignments (2 written reports + a handout and slides for the presentation) and (ii) the quality of the presentation itself.

Mark calculation method (including weighting of intermediary marks)

Both of the two grading components carry equal weight.

Language(s) of evaluation

  • english