Advanced Econometrics


Course Objective

This course introduces students to advanced econometric theory and
methods. Students will be guided through the frontier of econometric
theory and become familiar with state-of–the-art econometric models and

By the end of this course, participants will:
(1) have gained a profound and detailed understanding of advanced
econometric theory and methods;
(2) know how to design, estimate and analyze complex nonlinear dynamic
(3) have solved advanced theoretical and practical econometric
(4) understand the interplay between econometric techniques and modeling
(5) understand the proofs of asymptotic properties of important
estimators and test statistics.

Course Content

This course covers both theoretical and practical aspects of complex
dynamic econometric models that are used in the industry, by central
banks, governments, think tanks, and other research institutes.

The students will be introduced to stochastic theory that allows them to
fully understand the dynamic properties of complex models featuring
nonlinearities, time-varying parameters and latent variables. Important
concepts include invertibility, stationarity, dependence, ergodicity and
bounded moments

The students will also be introduced to advanced estimation theory that
allows them to "bring" state-of-the-art models to the data and conduct
inference on parameters under very general conditions. Important topics
include the existence, measurability, consistency and asymptotic
normality of extremum, M and Z estimators. We also cover advanced topics
in nonlinear model selection and specification, estimation and inference
under incorrect specification and metric selection.

From a practical perspective, the advanced methods and state-of-the-art
models are used for forecasting and policy analysis in a wide number of
applications ranging from finance to macroeconomics and data science.

Teaching Methods

Lectures and tutorials

Method of Assessment

Final exam and group assignment with Individual assessment.


Lecture notes on "Advanced Econometrics" written by the lecturer F.

Other sources:

Davidson J., "Econometric Theory", Blackwell Publishing, 2000.

van der Vaart A., "Asymptotic Statistics". Cambridge Series in
and Probabilistic Mathematics. Cambridge University Press, 2000.

White H., "Estimation, Inference and Specication Analysis". Econometrics
Society Monographs, 1996.

Lütkepohl H., "New Introduction to Multiple Time Series Analysis",
Springer, 2005.

Hamilton J. D., "Time Series Analysis", Princeton University Press.

Davidson J., "Stochastic Limit Theory". Advanced Texts in Econometrics,
Oxford University Press, 1994.

B. Potscher and I.R. Prucha, "Dynamic Nonlinear Econometric Models:
Asymptotic Theory". Springer-Verlag, 1997.

R. Gallant and H. White, "A Uni
ed Theory of Estimation and Inference
for Nonlinear Dynamic Models", Basil Blackwell Ltd., Oxford, 1987.

Hansen, B E, Econometrics. Manuscript, University of Wisconsin.2009.
Current URL:

Recommended background knowledge

This course presumes that students are familiar with basic probability
and statistics.
The theory and practice behind the simple linear
regression model should be well understood. Furthermore, the students
should have been introduced to time-series analysis. In particular, the
concept of stationarity and ARMA models should be familiar.

General Information

Course Code E_EORM_AECTR
Credits 6 EC
Period P1
Course Level 400
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator dr. F. Blasques Albergaria Amaral
Examiner dr. F. Blasques Albergaria Amaral
Teaching Staff dr. F. Blasques Albergaria Amaral

Practical Information

You need to register for this course yourself

Teaching Methods Lecture, Study Group
Target audiences

This course is also available as: