Please note: the information in the course manual is binding.
By the end of the course, students will be capable of:
• understanding and conducting basic field research methods;
• understanding spatial econometric analyses and performing basic spatial econometrics;
• critically ascertaining the need for spatial econometric methods;
• presenting results attractively yet academically correct on a poster;
• gathering and checking data from key statistical bureaus;
• reflecting upon the proper choice of method for a research project in economics and geography.
Over the past decades, several new methods have come up in regional science, some of which are directly related to theories, others to sheer computational power and mathematics. The debate between qualitative and case-study-based methods on the one hand, and quantitative analyses on the other also keeps raging on. We will cover three key methodological areas.|
Firstly, we cover field research methods: interviewing, gathering field observations (connected to an excursion), and surveying.
Secondly, we study the need for and use of spatial econometrics. This means we learn how to include space in standard econometric modelling in R: using spatial econometrics, we can see if phenomena vary over space, and whether they influence each other in space.
Thirdly, we have a wider debate on the appropriate use of methods and their possibilities, and we will look at several work-horse tools in the economic geographer’s toolkit. We discuss the merits of qualitative and quantitative methods, and spend three lectures on specific methods that are underrepresented in the programme as a whole, but which you will still encounter occur now and them – perhaps in your thesis or another research project, perhaps as a consumer of scientific research. You can suggest e cover whatever methods you would want to be discussed, as long as they’re not too obscure. If no requests are made, we look at panel data, shift-share analysis, gravity modelling, and survival analysis.
Finally, there is a guest lecture on data gathering handling and visualization. Throughout the course, you will work on a data collection assignment, which results in a paper.