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ECON-S428

Graduate econometrics I

academic year
2020-2021
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

See programme details

Language(s) of instruction

english

Course content

  1. Finite sample theory of linear regression models.

  2. Basic elements of asymptotic theory (notions of convergence and basic properties; laws of large numbers; central limit theorems)

  3. Large sample properties of estimators and tests in linear regression models.

  4. Asymptotics of M-estimators and in particular generalized method of moment estimators (asymptotic theory, examples, and related hypothesis tests).

  5. Basic elements of time series analysis (basic terminology, stationarity, linear time series models).

Objectives (and/or specific learning outcomes)

After successfully taking this course, students understand the theoretical framework, both finite sample as well as asymptotic, underlying linear regression models. Furthermore, they have a thorough understanding of generalized method of moment estimators and the corresponding hypothesis tests, and understand the basic elements of time series analysis. Students have applied the methods discussed in class themselves, understand how to implement these methods computationally, and can interpret the results obtained.

Prerequisites

Required knowledge and skills

A good understanding of elementary concepts in probability, statistics, linear algebra and calculus.

Teaching methods and learning activities

This course consists of weekly online lectures, in which the theoretical concepts are discussed and illustrated using practical examples; and weekly exercise sessions, during which we shall discuss theoretical as well as practical and computational problems in the form of weekly assignments to illustrate and supplement the topics discussed in the lectures. For the exercise sessions, students prepare solutions to weekly assignments, which are then presented online in class. Organizational details will be discussed in the first lecture on September 16, 2020, 16:00, via MS Teams.

Contribution to the teaching profile

This course provides a thorough overview of econometric methods. We start with a discussion of properties of estimators, tests and confidence intervals in linear regression models. Then, after introducing some basic elements of asymptotic theory, we derive large sample properties of procedures in linear regression models. We proceed to introduce the class of generalized method of moment estimators, and of related tests. In the last part of the course, we discuss fundamental elements of time series analysis, i.e., stationarity and autoregressive moving average models. We discuss computational and practical issues during the lectures, as well as in the problem sessions.

References, bibliography, and recommended reading

Hayashi, S. (2000). Econometrics. University Press Group Ltd.

Brockwell, P. and Davis, R.A. (1987). Time Series: Theory and Methods. Springer.

Newey, W. and McFadden, D. (1994). Large Sample Estimation and Hypothesis Testing. In: Handbook of Econometrics, Vol 4, Chapter 36.

Course notes

  • Podcast
  • Université virtuelle

Other information

Additional information

The course will entirely be held online. The main platform is Université virtuelle (UV), where you will be able to find lecture slides (after each online lecture) , and the weekly problem sets. The online lectures and exercise classes will be held via MS Teams (unless there are reasons to change the platform, which in this case will be communicated via UV).

Contacts

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

Campus

Outside campus ULB, Solbosch

Evaluation

Method(s) of evaluation

  • Other

Assessment is based on two components: (i) a written final exam, and (ii) the performance in the exercise classes.

Mark calculation method (including weighting of intermediary marks)

The written final exam carries 60 % of the weight, and the evaluation of the performance of the exercise classes carries 40 % of the weight. The evaluation of the exercise classes is based on the percentage of problems a student has successfully solved during the whole term. The written exam will be in January 2021, and there will be a written resit exam in August/September 2021.

Language(s) of evaluation

  • english

Programmes