Spatial Approaches to Panel Data in Agricultural Economics: A Climate Change Application
Kathy Baylis,
Nicholas D. Paulson and
Gianfranco Piras
Journal of Agricultural and Applied Economics, 2011, vol. 43, issue 3, 14
Abstract:
Panel data are used in almost all subfields of the agricultural economics profession. Furthermore, many research areas have an important spatial dimension. This article discusses some of the recent contributions made in the evolving theoretical and empirical literature on spatial econometric methods for panel data. We then illustrate some of these tools within a climate change application using a hedonic model of farmland values and panel data. Estimates for the model are provided across a range of nonspatial and spatial estimators, including spatial error and spatial lag models with fixed and random effects extensions. Given the importance of location and extensive use of panel data in many subfields of agricultural economics, these recently developed spatial panel methods hold great potential for applied researchers.
Keywords: Environmental Economics and Policy; Resource/Energy Economics and Policy (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)
Downloads: (external link)
https://ageconsearch.umn.edu/record/113518/files/jaae433ip2.pdf (application/pdf)
Related works:
Journal Article: Spatial Approaches to Panel Data in Agricultural Economics: A Climate Change Application (2011) 
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:joaaec:113518
DOI: 10.22004/ag.econ.113518
Access Statistics for this article
More articles in Journal of Agricultural and Applied Economics from Southern Agricultural Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().