USING MULTIPLE NEIGHBORING INTERACTION EFFECTS IN SPATIAL REGRESSION SPECIFICATIONS TO REDUCE OMITTED VARIABLE BIAS

Hugo Storm and Thomas Heckelei

No 244763, 56th Annual Conference, Bonn, Germany, September 28-30, 2016 from German Association of Agricultural Economists (GEWISOLA)

Abstract: A major challenge in the analysis of micro level spatial interaction is to distinguish actual interactions from the effects of spatially correlated omitted variables. We consider a spatially lagged explanatory model (SLX) employing two spatial weighting matrices differentiating between local and regional neighborhoods. We empirically analyze spatial interaction between individual farms in Norway and additionally perform Monte Carlo simulations exploring the model’s performance under different data settings. Results show that including two spatial weighting matrices can indeed reduce the bias resulting from omitted variables. The empirical application identifies different local and regional spatial interdependencies of direct payments with opposite sign.

Keywords: Agricultural and Food Policy; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 39
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/244763/files/Storm.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:gewi16:244763

DOI: 10.22004/ag.econ.244763

Access Statistics for this paper

More papers in 56th Annual Conference, Bonn, Germany, September 28-30, 2016 from German Association of Agricultural Economists (GEWISOLA) Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().