Institution: see Organisers & Acknowledgements
Program of study: International Research Workshop
Lecturer: Nisar Ahmad, Timo Friedel Mitze & Torben Dall Schmidt (University of Southern Denmark)
Date:
03.10.2013, 09:30 – 17:30
Room: n.s.
Max. number of participants: 20
Semester periods per week: n.s.
Credit Points: 5 CP for participating in the whole IRWS
Language of instruction: English
Contents:
The course is basically divided into two parts: Part 1) Analyzing panel data. Part 2) Spatial Data Analysis
Part 1): Structure of the Panel Data:
This part of the course is an introduction to the panel data analysis and it provides some insights into why we use panel data. What kinds of models are available for panel data and how do we estimate such models. It also covers some extensions to the basic panel data models and finally there will be a session where you will learn how to estimate panel data using STATA.
Part 2): Spatial Data Analysis
In research fields such as regional science, quantitative sociology and business analysis as well as real estate, labor and health economics (to name just a few), researchers are increasingly aware of the fact that “space matters”. Thus, the goal of this workshop module is to equip participants with the basic knowledge about methods and tools currently available in “spatial statistics” and “spatial econometrics”. Besides presenting the general logic and theoretical foundations of these modeling approaches for variables with an explicit geographical context, a strong focus lies on illustrating the potential for applied work with these tools in the software package STATA. The module is structured as follows: After a brief introduction of the historical evolution of spatial data analysis, different research settings in economics and related research fields are outlined, which may call for the explicit use of spatial estimation techniques, for instance, in order to identify the importance of space-time autocorrelations and neighboring effects (spatial spillovers). Following this introduction, the concept of the spatial weighting matrix is introduced and statistical approaches to measure and visualize the degree of spatial dependence for a variable under study are presented. Moving from univariate to multivariate modeling techniques, the course then derives estimation techniques used in the field of spatial econometrics and links this theoretical knowledge with hands-on applications for different spatial datasets. Finally, to serve as an outlook on future research possibilities, state-of-the-art concepts such as spatial panel data models and spatial limited dependent variable models will be presented.
Datasets and STATA ado-files will be provided ahead of the course and should be installed on the participants’ computers.
You have to register for the 7th International Research Workshop to participate in this course.