Last week, I spent three days in a workshop (or short course) on spatial econometrics at the University of Colorado‘s interdisciplinary population center, the Institute for Behavioral Science. At the beginning of last semester, many of my methods students expressed interest in doing their research papers on a topic with a significant spatial component. I would have loved for them to incorporate spatial analysis, but it was a topic I had touched only tangentially and didn’t feel qualified to learn it at the same time as teaching that (incredibly demanding) course for the first(ish) time. In addition, having just attended the PAA meetings in San Francisco, I’ve been looking for ways to expand my econometric skills and incorporate spatial data into my work. It was really fantastic. I don’t know whether they’ll be hosting the event again next summer, but do keep a lookout if you’re interested. I thought it was extremely helpful. And fun (see nerdy tweets from last week about loving matrix algebra). Paul Voss, of the University of North Carolina’s Population Center, Elisabeth Root, and Seth Spielman were all great.
I posted a short introduction to spatial econometrics last week based on my readings for the first class and am now excited to share some of the things I learned, so over the next few weeks, I’ll post some of my thoughts in a mini-series on spatial econometrics. This post will be updated with a list of posts in the series, so do follow along.
Experts, please keep me honest! This stuff is very cool, but I’m still a newbie.
Preliminary outline (subject to change):
- An introduction to Spatial Econometrics
- Spatial Autocorrelation is Not Causation
- The Weights Matrix for Spatial Analysis
- Some Notes on Terminology in Spatial Econometrics