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Bandwidth Selection For Spatial Hac And Other Robust Covariance Estimators

  • Dayton M. Lambert

    ()

    (Department of Agricultural Economics, University of Tennessee)

  • Raymond J.G.M. Florax

    ()

    (Department of Agricultural Economics, Purdue University)

  • Seong-Hoon Cho

    ()

    (Department of Agricultural Economics, University of Tennessee)

This research note documents estimation procedures and results for an empirical investigation of the performance of the recently developed spatial, heteroskedasticity and autocorrelation consistent (HAC) covariance estimator calibrated with different kernel bandwidths. The empirical example is concerned with a hedonic price model for residential property values. The first bandwidth approach varies an a priori determined plug-in bandwidth criterion. The second method is a data driven cross-validation approach to determine the optimal neighborhood. The third approach uses a robust semivariogram to determine the range over which residuals are spatially correlated. Inference becomes more conservative as the plug-in bandwidth is increased. The data-driven approaches prove valuable because they are capable of identifying the optimal spatial range, which can subsequently be used to inform the choice of an appropriate bandwidth value. In our empirical example, pertaining to a standard spatial model and ditto dataset, the results of the data driven procedures can only be reconciled with relatively high plug-in values (n0.65 or n0.75). The results for the semivariogram and the cross-validation approaches are very similar which, given its computational simplicity, gives the semivariogram approach an edge over the more flexible cross-validation approach.

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Paper provided by Purdue University, College of Agriculture, Department of Agricultural Economics in its series Working Papers with number 08-10.

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Length: 25 pages
Date of creation: 2008
Date of revision:
Handle: RePEc:pae:wpaper:08-10
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Web page: http://www.agecon.purdue.edu/

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  1. Luc Anselin & Nancy Lozano-Gracia, 2008. "Errors in variables and spatial effects in hedonic house price models of ambient air quality," Empirical Economics, Springer, vol. 34(1), pages 5-34, February.
  2. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
  3. Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
  4. 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 509-33, May.
  5. Dubin, Robin A., 1992. "Spatial autocorrelation and neighborhood quality," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 433-452, September.
  6. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
  7. McMillen, Daniel P., 1996. "One Hundred Fifty Years of Land Values in Chicago: A Nonparametric Approach," Journal of Urban Economics, Elsevier, vol. 40(1), pages 100-124, July.
  8. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
  9. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, March.
  10. Lambert, D. M. & Clark, C. D. & Wilcox, M. D. & Park, W. M., 2007. "Do Migrating Seniors Affect Business Establishment and Job Growth? An Empirical Look at Southeastern Nonmetropolitan Counties, 2000-2004," The Review of Regional Studies, Southern Regional Science Association, vol. 37(2), pages 251-78.
  11. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
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