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Using Multiple Neighboring Interaction Effects In Spatial Regression Specifications To Reduce Omitted Variable Bias

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  • Storm, Hugo
  • Heckelei, Thomas

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.

Suggested Citation

  • Storm, Hugo & Heckelei, Thomas, 2016. "Using Multiple Neighboring Interaction Effects In Spatial Regression Specifications To Reduce Omitted Variable Bias," 56th Annual Conference, Bonn, Germany, September 28-30, 2016 244763, German Association of Agricultural Economists (GEWISOLA).
  • Handle: RePEc:ags:gewi16:244763
    DOI: 10.22004/ag.econ.244763
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    File URL: https://ageconsearch.umn.edu/record/244763/files/Storm.pdf
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    References listed on IDEAS

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    1. Stephen Gibbons & Henry G. Overman, 2012. "Mostly Pointless Spatial Econometrics?," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 172-191, May.
    2. Andrea Zimmermann & Thomas Heckelei, 2012. "Structural Change of European Dairy Farms – A Cross-Regional Analysis," Journal of Agricultural Economics, Wiley Blackwell, vol. 63(3), pages 576-603, September.
    3. Hugo Storm & Klaus Mittenzwei & Thomas Heckelei, 2015. "Direct Payments, Spatial Competition, and Farm Survival in Norway," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(4), pages 1192-1205.
    4. Christoph R. Weiss, 1999. "Farm Growth and Survival: Econometric Evidence for Individual Farms in Upper Austria," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(1), pages 103-116.
    5. Guastella, Gianni & Moro, Daniele & Sckokai, Paolo & Veneziani, Mario, 2014. "The Capitalisation of Fixed per hectare Payment into Land Rental Prices: a Spatial Econometric Analysis of Regions in EU," 2014 Third Congress, June 25-27, 2014, Alghero, Italy 173093, Italian Association of Agricultural and Applied Economics (AIEAA).
    6. Arne Hallam, 1991. "Economies of Size and Scale in Agriculture: An Interpretive Review of Empirical Measurement," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 13(1), pages 155-172.
    7. Mikaël Akimowicz & Marie-Benoît Magrini & Aude Ridier & Jacques-Eric Bergez & Denis Requier-Desjardins, 2013. "What Influences Farm Size Growth? An Illustration in Southwestern France," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 35(2), pages 242-269.
    8. Mosnier, Claire & Wieck, Christine, 2010. "Determinants of spatial dynamics of dairy production: a review," Discussion Papers 162896, University of Bonn, Institute for Food and Resource Economics.
    9. Doris Läpple & Hugh Kelley, 2015. "Spatial dependence in the adoption of organic drystock farming in Ireland," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 42(2), pages 315-337.
    10. Holloway, Garth & Shankar, Bhavani & Rahman, Sanzidur, 2002. "Bayesian spatial probit estimation: a primer and an application to HYV rice adoption," Agricultural Economics, Blackwell, vol. 27(3), pages 383-402, November.
    11. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    12. David J. Lewis & Bradford L. Barham & Brian Robinson, 2011. "Are There Spatial Spillovers in the Adoption of Clean Technology? The Case of Organic Dairy Farming," Land Economics, University of Wisconsin Press, vol. 87(2), pages 250-267.
    13. LeSage, James P. & Pace, Robert Kelley, 2011. "Pitfalls in Higher Order Model Extensions of Basic Spatial Regression Methodology," The Review of Regional Studies, Southern Regional Science Association, vol. 41(1), pages 13-26, Summer.
    14. Feichtinger, Paul & Salhofer, Klaus, 2014. "The common agricultural policy of the EU and agricultural land prices - a spatial econometric approach for Bavaria," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182751, European Association of Agricultural Economists.
    15. Solmaria Halleck Vega & J. Paul Elhorst, 2015. "The Slx Model," Journal of Regional Science, Wiley Blackwell, vol. 55(3), pages 339-363, June.
    16. Eva Schmidtner & Christian Lippert & Barbara Engler & Anna Maria Häring & Jaochim Aurbacher & Stephan Dabbert, 2012. "Spatial distribution of organic farming in Germany: does neighbourhood matter?," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 39(4), pages 661-683, September.
    17. Schmidtner, Eva & Lippert, Christian & Dabbert, Stephan, 2015. "Does Spatial Dependence Depend on Spatial Resolution? – An Empirical Analysis of Organic Farming in Southern Germany," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 64(03), September.
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