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Bandwidth Selection For Spatial Hac And Other Robust Covariance Estimators

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Author Info

  • 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)

Abstract

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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Bibliographic Info

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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Keywords: spatial HAC; semivariogram; bandwidth; hedonic model;

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  1. 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.
  2. Dubin, Robin A., 1992. "Spatial autocorrelation and neighborhood quality," Regional Science and Urban Economics, Elsevier, Elsevier, vol. 22(3), pages 433-452, September.
  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. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, Elsevier, vol. 26(1), pages 77-104, February.
  5. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, Elsevier, vol. 92(1), pages 1-45, September.
  6. 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, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
  7. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780195060119, October.
  8. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, Elsevier, vol. 140(1), pages 131-154, September.
  9. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, Econometric Society, vol. 59(3), pages 817-58, May.
  10. Luc Anselin & Nancy Lozano-Gracia, 2008. "Errors in variables and spatial effects in hedonic house price models of ambient air quality," Empirical Economics, Springer, Springer, vol. 34(1), pages 5-34, February.
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Cited by:
  1. Christian Helmers & Manasa Patnam, 2011. "Does the rotten child spoil his companion?: spatial peer effects among children in rural india," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library 33558, London School of Economics and Political Science, LSE Library.
  2. Jacint Balaguer & José Pernías, 2011. "Measuring the effect of local competition on prices in a context of spatial differentiation," Letters in Spatial and Resource Sciences, Springer, Springer, vol. 4(2), pages 109-116, July.
  3. Jacint Balaguer Coll & José C. Pernías, 2010. "Spatial density, average prices and price dispersion. Evidence from the Spanish hotel industry," Working Papers. Serie EC, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) 2010-03, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).

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