Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Hafner Christian;
Language
French
Content
Summary
The econometric techniques used in economic geography have dramatically improved during the last decade. Moreover, similar statistical problems arise in the various fields of physical geography. The objective of the course is to allow a geographer acquainted with a preliminary background in statistics to meet the level of statistical requirements for understanding articles and for publishing in high-ranking journals. The course focuses on linear models. A great deal of efforts is placed on the statistical validation of these models: selection of variables, functional form, endogeneity problems, temporal and spatial autocorrelation in errors terms, selection biases, etc. To ignore misspecifications of the model may entail spurious interpretations and unreliable predictions. Several techniques that allow to overcome some of these issues are studied: weighted least-squares, feasible generalized least squares, instrumental variables, autoregressive and correlated error models, etc. An initiation to the R language is also provided, as well as an exploration of the related spatial statistics libraries.
Methods
Theoretical developments and practical illustrations on the computer alternate during the class.
Teaching methods
lectures on theory and illustrations on computers
Evaluation methods
The evaluation is based on a test of knowledge (short-answers questions), a personal project on a realistic dataset, the presentation of selected recent articles drawn high-ranked journals (Regional Science and Urban Economics, Journal of Urban Economics,
).
Other information
Prerequisite GEO1341 Modélisation statistique en géographie (or similar).
Online resources
Every note, chunk of R code, or dataset used in the course is available on the moodle site associated with this course.
Bibliography
- R Bivand, E Pebesma and V Gómez-Rubio, Applied Spatial Data Analysis with R, Springer, New York, 2008.
- MJ Crawley, Statistics: An Introduction Using R, John Wiley, 2005.
- MJ Crawley, The R Book, John Wiley, 2007.
- O Schabenberger and C Gotway, Statistical Methods for Spatial Data Analysis, Chapman & Hall, 2005.
- WN Venables and BD Ripley, Modern Applied Statistics with S (4th edition), Springer, 2002.
- M Verbeek, A Guide to Modern Econometrics, John Wiley, 2000.
Faculty or entity
GEOG
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Interdisciplinary Advanced Master in Science and Management of the Environment and Sustainable Development
Master [120] in Geography : General
Master [120] in Statistic: General
Master [120] in Geography : Climatology
Master [60] in Geography : General
Advanced Master in Quantitative Methods in the Social Sciences