2012/04/17 by Stephen Gibbons, Henry G. Overman · 12 citations
Economics, Econometrics and Finance · Mathematics · #Artificial intelligence #Causality (physics) #Computer science #Econometric model #Econometrics #Economics #Estimator #Identification (biology) #Instrumental variable #Mathematics #Regional Economic and Spatial Analysis #Regional Economics and Spatial Analysis #Rendering (computer graphics) #Spatial analysis #Spatial and Panel Data Analysis #Spatial econometrics #Statistics
paper · doi:10.1111/j.1467-9787.2012.00760.x
published in Journal of Regional Science 52(2), 172-191 (Wiley)
openalex publication_date 2012/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
ABSTRACT We argue that identification problems bedevil applied spatial economic research. Spatial econometrics usually solves these problems by deriving estimators assuming that functional forms are known and by using model comparison techniques to let the data choose between competing specifications. We argue that in many situations of interest this achieves, at best, only very weak identification. Worse, in many cases, such an approach will be uninformative about the causal economic processes at work, rendering much applied spatial econometric research “pointless,” unless the main aim is description of the data. We advocate an alternative approach based on the “experimentalist paradigm” which puts issues of identification and causality at center stage.