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Double-Length Regression tests for testing functional forms and spatial error dependence

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  • Le, Canh Quang
  • Li, Dong

Abstract

In this paper we derive test statistics based on Double-Length Regressions (DLRs) for testing functional forms and spatial error dependence. These DLR tests are computationally simple. Their Monte Carlo performance is similar to that of the Hessian-based Lagrangian Multiplier tests.

Suggested Citation

  • Le, Canh Quang & Li, Dong, 2008. "Double-Length Regression tests for testing functional forms and spatial error dependence," Economics Letters, Elsevier, vol. 101(3), pages 253-257, December.
  • Handle: RePEc:eee:ecolet:v:101:y:2008:i:3:p:253-257
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    References listed on IDEAS

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    1. Davidson, Russell & MacKinnon, James G, 1984. "Model Specification Tests Based on Artificial Linear Regressions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(2), pages 485-502, June.
    2. Florax, Raymond & Folmer, Henk, 1992. "Specification and estimation of spatial linear regression models : Monte Carlo evaluation of pre-test estimators," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 405-432, September.
    3. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    4. Cem Ertur & Julie Le Gallo & Catherine Baumont, 2006. "The European Regional Convergence Process, 1980-1995: Do Spatial Regimes and Spatial Dependence Matter?," International Regional Science Review, , vol. 29(1), pages 3-34, January.
    5. Davidson, Russell & MacKinnon, James G, 1988. "Double Length Artificial Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 50(2), pages 203-217, May.
    6. T J Fik & G F Mulligan, 1998. "Functional Form and Spatial Interaction Models," Environment and Planning A, , vol. 30(8), pages 1497-1507, August.
    7. Badi Baltagi & Dong Li, 2001. "Double Length Artificial Regressions For Testing Spatial Dependence," Econometric Reviews, Taylor & Francis Journals, vol. 20(1), pages 31-40.
    8. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
    9. 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.
    10. Boxall, Peter C. & Chan, Wing H. & McMillan, Melville L., 2005. "The impact of oil and natural gas facilities on rural residential property values: a spatial hedonic analysis," Resource and Energy Economics, Elsevier, vol. 27(3), pages 248-269, October.
    11. Donald J. Lacombe & Timothy M. Shaughnessy, 2007. "Accounting for Spatial Error Correlation in the 2004 Presidential Popular Vote," Public Finance Review, , vol. 35(4), pages 480-499, July.
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    Cited by:

    1. Li Dong & Le Canh, 2010. "Nonlinearity and Spatial Lag Dependence: Tests Based on Double-Length Regressions," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-18, June.
    2. Badi Baltagi & Long Liu, 2014. "Testing for spatial lag and spatial error dependence using double length artificial regressions," Statistical Papers, Springer, vol. 55(2), pages 477-486, May.
    3. Badi H. Baltagi & Long Liu, 2015. "Testing for Spacial Lag and Spatial Error Dependence in a Fixed Effects Panel Data Model Using Double Length Artificial Regressions," Center for Policy Research Working Papers 183, Center for Policy Research, Maxwell School, Syracuse University.
    4. Patrick J. Walsh & J. Walter Milon & David O. Scrogin, 2011. "The Spatial Extent of Water Quality Benefits in Urban Housing Markets," Land Economics, University of Wisconsin Press, vol. 87(4), pages 628-644.

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