Applied Econometrics for Urban, Transport and Environmental Economics


Course Objective

The main objective of this course is to provide an overview of
econometric research methods in spatial economics and to teach you how
to apply these methods to real-world data.

Course Content

Public policies need to be evaluated in order to understand their
effectiveness and correct validation of economic theory can only be
achieved with empirical research. The main objective of this course is
to provide an overview of econometric research methods in urban,
transport and environmental economics and to teach you how to apply
these methods to real-world data. After following this course, you will:
• have an advanced understanding of the mathematical and statistical
concepts underlying regression analysis;
• understand the importance of and difficulties in estimating causal
effects as opposed to correlations in spatial economics problems;
• know how to appropriately interpret regression results of various
estimators and know which one to apply in particular situations,
depending on (i) the nature of the data (cross-sectional / panel /
discrete data) and (ii) the task at hand (i.e., valuation of public
policies, testing of economic theories or estimating parameters as
derived from theory);
• understand and know how to apply techniques that are commonly in use
in urban, transport and environmental economics and policy: spatial
econometrics, discrete choice models and quasi-experimental research
• be able to apply these methods independently to typical datasets in
spatial economics and other domains (including labour economics and
public economics) using the software package STATA.

Teaching Methods

Lectures (12) and tutorials (6)

Method of Assessment

Written examination (75%): some questions on the theoretical
prerequisites but mainly interpretation of regression outputs and
sketches of solution strategies for the estimation of particular
parameters in well-defined situations.

Assignment (25%) in small groups: Assignments are to be handed in before
the tutorials and discussed there. Some assignments relate to the
derivation of theoretical propositions of the estimators and their
properties, but the main focus is hands-on computer exercises applying
the theoretical concepts to real-world data using the software package
STATA and correct subsequent interpretation of the results.


• Stock, J.H., Watson, M.W. (2011). Introduction to econometrics. Upper
Saddle River, NJ: Pearson Education.
• Train, K. (2009). Discrete choice methods with simulation. Cambridge:
Cambridge University Press. Chapters 2 and 3.
• Syllabus on Spatial Econometrics.
• Gibbons, S., Overman, H. G. (2012). Mostly Pointless Spatial
Econometrics? Journal of Regional Science, 52(2), 172-191.
• Angrist, J.D., Pischke, J.-D. (2009). Mostly Harmless Econometrics: An
Empiricists Companion. Princeton: Princeton University Press. Chapters
1, 2, 3.1, 4.1, 5.1, 6.1, 8.1 and 8.2.

Except for Stock and Watson, the accompanying literature will be made
available on Canvas.

Additional Information

Students are strongly(!) advised to follow the Math Refresher and
Introduction to STATA courses that are given from August 27 to 31,
during the last week before the courses start officially

Recommended background knowledge

An active knowledge of mathematical tools and econometric techniques is
required. Please apply for the the Math and STATA refresher otherwise.

General Information

Course Code E_STR_AEUTE
Credits 6 EC
Period P1
Course Level 400
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator dr. H.R.A. Koster
Examiner dr. H.R.A. Koster
Teaching Staff dr. H.R.A. Koster
dr. T. de Graaff
S. Sovago
dr. S. Dobbelaere

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Study Group, Lecture
Target audiences

This course is also available as: