Winter Semester 26/27

Tutorial

Recent Developments in Econometrics

Lecturer:
M.Sc. (RGS Econ) Ignacio Moreira-Lara
Contact:
M.Sc. (RGS Econ) Ignacio Moreira-Lara
Term:
Winter Semester 2026/2027
Cycle:
Block course
Time:
See course description
Room:
See course description
Language:
English
Moodle:
Lecture in Moodle
LSF:
Lecture in LSF
Linked Lectures:
Participants
Module Recent Developments in Econometrics in the degree programs

Important Notes:

DateTimeRoomSession
Mon 19 Oct 202614:00-18:00R11 T08 C01Lecture
Wed 21 Oct 202614:00-18:00R11 T07 C73Lecture
Fri 23 Oct 202610:00-12:00R11 T06 C85Lecture
Fri 23 Oct 202614:00-18:00R11 T06 C85Lecture
Thu 29 Oct 202610:00-14:00R11 T08 C01Exercise
Fri 30 Oct 202610:00-14:00R11 T07 C73Exercise
Mon 7 Dec 202614:00-18:00R11 T06 C85Lecture
Wed 9 Dec 202610:00-12:00R12 R06 A84Lecture
Wed 9 Dec 202614:00-18:00R11 T08 C01Lecture
Fri 11 Dec 202614:00-18:00R11 T06 C85Lecture
Thu 17 Dec 202610:00-14:00R11 T08 C01Exercise
Fri 18 Dec 202610:00-14:00R11 T08 C01Exercise

Christmas break

Fri 22 Jan 202710:00-16:00R11 T07 C73Exercise
Fri 29 Jan 202710:00-14:00R11 T07 C73Exercise
Fri 5 Feb 202710:00-14:00R11 T07 C73Exercise

Description:

The course is intended to provide a comprehensive overview of econometric and statistical methods. The components of an econometric model are presented and discussed in a rigorous mathematical framework.

Learning Targets:

Students

  • Understand the linear regression method’s properties in finite sample and asymptotic limit.
  • Generalize the linear regression model for the presence of endogeneity and other forms of data
  • Understand the construction of a theoretical econometric study

Outline:

  • The linear regression model: least squares, the Frisch-Waugh-Lovell theorem, finite-sample prop-
    erties of OLS and the Gauss-Markov theorem, R2, outliers and leverage, prediction, generalized
    least squares.
  • Asymptotic theory: convergence in probability and in distribution, laws of large numbers, central
    limit theorems, the continuous mapping theorem, the delta method, Op and op notation.
  • Asymptotics in the linear model: hypothesis tests and confidence regions, robust and cluster-
    robust standard errors, model selection, specification and nonnested tests, structural break tests,
    autocorrelation, linear projection.
  • The generalized method of moments: endogeneity and instruments, GMM estimation and in-
    ference, serially correlated moments and kernel estimates of the long-run covariance matrix.
  • Multiple-equation GMM: systems of equations, seemingly unrelated regressions (SUR), 2SLS
    and 3SLS.
  • Panel data: random-effects and fixed-effects estimators, the Hausman test, a dynamic panel
    data model.
  • Extremum estimators: nonlinear least squares, nonlinear GMM and maximum likelihood, consis-
    tency and asymptotic normality, the classical tests (Wald, likelihood ratio, Lagrange multiplier),
    limited dependent variables and binary panel data models

Literature:

  • Davidson R, MacKinnon JG. 1993. Estimation and Inference in Econometrics. New York: Oxford University Press.
  • Davidson R, MacKinnon JG. 2004. Econometric Theory and Methods. New York: Oxford University Press.
  • Greene WH. 2018. Econometric Analysis. New York: Pearson, 8th edn.
  • Hayashi F. 2000. Econometrics. Princeton: Princeton University Press.
  • Verbeek M. 2017. A Guide to Modern Econometrics. Hoboken: John Wiley & Sons, 5th edn.
  • Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press, 2nd edn.

Methods of Assessment:

The final exam is offered on the two dates listed above and lasts 90 min for 6-credit students and 120 min for 9-credit students. Questions may contain R code, output or figures, which you will be asked to interpret; you will not be asked to write code. Students will be evaluated on their understanding of a theoretical econometrics study not on their programming skills. 

Formalities:

Knowledge of econometric methods taught in an undergraduate course is needed. More specifically,
we expect you to have a background in or be familiar with:

  • Introductory econometrics topics: linear regression, endogenous regressors and IV
  • Statistics: densities, (conditional) distributions, expectations, some specific families (normal, t,
    F, etc.)
  • Econometric/statistical software: we shall use R (https://cran.r-project.org), so basic
    knowledge should come in handy, but is not strictly necessary.
  • Rigorous linear algebra, integration, differentiation and algebra.