IDEAS home Printed from https://ideas.repec.org/p/apu/wpaper/2014-02.html
   My bibliography  Save this paper

The effects of scale, space and time on the predictive accuracy of land use models

Author

Listed:
  • Jean-Sauveur Ay
  • Raja Chakir
  • Julie Le Gallo

Abstract

The econometric literature about modeling land use choices is highly heterogeneous with respect to the scale of the data, and to the structure of the models in terms of the effects of space and time. This paper proposes a joint evaluation of each of these three elements by estimating a broad spectrum of individual and aggregate, spatial and aspatial, short and long run econometric models on the same detailed French dataset. Considering four land use classes (arable crops, pasture, forest, and urban), all the models are compared in terms of both in- and out-of-sample predictive accuracy. We argue that the aggregate scale allows to model more effectively the effect of space by using spatial econometric models. We show that modeling spatial autocorrelation allow to have very accurate predictions which can even outperform individual models when the appropriate predictors are used. We also found some strong interactions between the effects of scale, space and time which can be of major interest for applied researchers.

Suggested Citation

  • Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
  • Handle: RePEc:apu:wpaper:2014/02
    as

    Download full text from publisher

    File URL: https://www6.versailles-grignon.inra.fr/economie_publique/Media/fichiers/Working-Papers/Working-Papers-2014/WP_2014_02
    Download Restriction: no

    References listed on IDEAS

    as
    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Douglas J. Miller & Andrew J. Plantinga, 1999. "Modeling Land Use Decisions with Aggregate Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(1), pages 180-194.
    3. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    4. Man Li & JunJie Wu & Xiangzheng Deng, 2013. "Identifying Drivers of Land Use Change in China: A Spatial Multinomial Logit Model Analysis," Land Economics, University of Wisconsin Press, vol. 89(4), pages 632-654.
    5. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    6. Carlo Fezzi & Ian J. Bateman, 2011. "Structural Agricultural Land Use Modeling for Spatial Agro-Environmental Policy Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(4), pages 1168-1188.
    7. Wu, JunJie & Adams, Richard M., 2002. "Micro Versus Macro Acreage Response Models: Does Site-Specific Information Matter?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(01), July.
    8. Raja Chakir & Olivier Parent, 2009. "Determinants of land use changes: A spatial multinomial probit approach," Papers in Regional Science, Wiley Blackwell, vol. 88(2), pages 327-344, June.
    9. Lewis, David J., 2010. "An economic framework for forecasting land-use and ecosystem change," Resource and Energy Economics, Elsevier, pages 98-116.
    10. Nancy E. Bockstael, 1996. "Modeling Economics and Ecology: The Importance of a Spatial Perspective," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(5), pages 1168-1180.
    11. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    12. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    13. Florax, Raymond J. G. M. & Folmer, Hendrik & Rey, Sergio J., 2003. "Specification searches in spatial econometrics: the relevance of Hendry's methodology," Regional Science and Urban Economics, Elsevier, vol. 33(5), pages 557-579, September.
    14. Smirnov, Oleg A., 2010. "Modeling spatial discrete choice," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 292-298, September.
    15. Klier, Thomas & McMillen, Daniel P, 2008. "Clustering of Auto Supplier Plants in the United States," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 460-471.
    16. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
    17. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, December.
    18. Elena G. Irwin, 2010. "New Directions For Urban Economic Models Of Land Use Change: Incorporating Spatial Dynamics And Heterogeneity," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 65-91.
    19. Anselin, Luc, 2002. "Under the hood Issues in the specification and interpretation of spatial regression models," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 27(3), November.
    20. David J. Lewis & Andrew J. Plantinga, 2007. "Policies for Habitat Fragmentation: Combining Econometrics with GIS-Based Landscape Simulations," Land Economics, University of Wisconsin Press, vol. 83(2), pages 109-127.
    21. Ana Angulo & F. Trívez, 2010. "The impact of spatial elements on the forecasting of Spanish labour series," Journal of Geographical Systems, Springer, vol. 12(2), pages 155-174, June.
    22. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    23. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    24. Ruben N. Lubowski & Andrew J. Plantinga & Robert N. Stavins, 2008. "What Drives Land-Use Change in the United States? A National Analysis of Landowner Decisions," Land Economics, University of Wisconsin Press, vol. 84(4), pages 529-550.
    25. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2012. "Forecasting with spatial panel data," Computational Statistics & Data Analysis, Elsevier, pages 3381-3397.
    26. Jean-Sauveur Ay & Raja Chakir & Luc Doyen & Frédéric Jiguet & Paul Leadley, 2014. "Integrated models, scenarios and dynamics of climate, land use and common birds," Climatic Change, Springer, vol. 126(1), pages 13-30, September.
    27. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
    28. Douglas J. Miller, 1999. "An Econometric Analysis of the Costs of Sequestering Carbon in Forests," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 812-824.
    29. Andrew J. Plantinga, 1996. "The Effect of Agricultural Policies on Land Use and Environmental Quality," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(4), pages 1082-1091.
    30. Wu, JunJie, 2014. "The Oxford Handbook of Land Economics," OUP Catalogue, Oxford University Press, number 9780199763740 edited by Duke, Joshua M..
    31. Anselin, Luc, 2007. "Spatial econometrics in RSUE: Retrospect and prospect," Regional Science and Urban Economics, Elsevier, vol. 37(4), pages 450-456, July.
    32. Kathleen P. Bell & Nancy E. Bockstael, 2000. "Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Microlevel Data," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 72-82, February.
    33. Nazneen Ferdous & Chandra Bhat, 2013. "A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns," Journal of Geographical Systems, Springer, vol. 15(1), pages 1-29, January.
    34. Lubowski, Ruben N. & Plantinga, Andrew J. & Stavins, Robert N., 2006. "Land-use change and carbon sinks: Econometric estimation of the carbon sequestration supply function," Journal of Environmental Economics and Management, Elsevier, vol. 51(2), pages 135-152, March.
    35. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    36. Michael Brady & Elena Irwin, 2011. "Accounting for Spatial Effects in Economic Models of Land Use: Recent Developments and Challenges Ahead," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(3), pages 487-509, March.
    37. John Mullahy, 2010. "Multivariate Fractional Regression Estimation of Econometric Share Models," NBER Working Papers 16354, National Bureau of Economic Research, Inc.
    38. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
    39. Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, January.
    40. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    41. Chakir, Raja & Le Gallo, Julie, 2013. "Predicting land use allocation in France: A spatial panel data analysis," Ecological Economics, Elsevier, vol. 92(C), pages 114-125.
    42. Stavins, Robert N & Jaffe, Adam B, 1990. "Unintended Impacts of Public Investments on Private Decisions: The Depletion of Forested Wetlands," American Economic Review, American Economic Association, vol. 80(3), pages 337-352, June.
    43. Miller, Douglas & Plantinga, Andrew J., 1999. "Modeling Land Use Decisions with Aggregate Data," Staff General Research Papers Archive 1487, Iowa State University, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chakir, Raja & Lungarska, Anna, 2015. "Agricultural land rents in land use models: a spatial econometric analysis," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212641, European Association of Agricultural Economists.

    More about this item

    Keywords

    Land use models; spatial econometrics; predictive accuracy; aggregate and individual data;

    JEL classification:

    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q24 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Land
    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:apu:wpaper:2014/02. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Régis Grateau). General contact details of provider: http://edirc.repec.org/data/epinrfr.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.