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Double Length Artificial Regressions For Testing Spatial Dependence

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  • Badi Baltagi
  • Dong Li

Abstract

This paper derives two simple artificial Double Length Regressions (DLR) to test for spatial dependence. The first DLR tests for spatial lag dependence while the second DLR tests for spatial error dependence. Both artificial regressions utilize only least squares residuals of the restricted model and are therefore easy to compute. These tests are illustrated using two simple examples. In addition, Monte Carlo experiments are performed to study the small sample performance of these tests. As expected, these DLR tests have similar performance to their corresponding LM counterparts.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 20 (2001)
Issue (Month): 1 ()
Pages: 31-40

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Handle: RePEc:taf:emetrv:v:20:y:2001:i:1:p:31-40

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Related research

Keywords: Double length regressions; Spatial dependence; Lagrange multiplier; JEL Classification: C12; C21; R15;

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Cited by:
  1. Simlai, Prodosh, 2014. "Estimation of variance of housing prices using spatial conditional heteroskedasticity (SARCH) model with an application to Boston housing price data," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 17-30.
  2. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
  3. Robin Pope, 2009. "Risk starvation contributes to dementias and depressions: Whiffs of danger are the antidote," Bonn Econ Discussion Papers bgse28_2009, University of Bonn, Germany.
  4. 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.
  5. Badi H. Baltagi & Long Liu, 2012. "Testing for Spatial Lag and Spatial Error Dependence Using Double Length Artificial Regressions," Center for Policy Research Working Papers 147, Center for Policy Research, Maxwell School, Syracuse University.
  6. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
  7. Keller, Wolfgang & Shiue, Carol H., 2007. "The origin of spatial interaction," Journal of Econometrics, Elsevier, vol. 140(1), pages 304-332, September.
  8. Joachim Möller, 2009. "Regional variations in the price of building land: a spatial econometrics approach for West Germany," The Annals of Regional Science, Springer, vol. 43(1), pages 113-132, March.
  9. Benjamin Born & Jörg Breitung, 2011. "Simple regression‐based tests for spatial dependence," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 330-342, 07.
  10. Joachim Möller & Alisher Aldashev, 2006. "Interregional differences in labor market participation," Jahrbuch für Regionalwissenschaft, Springer, vol. 26(1), pages 25-50, March.
  11. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
  12. Harry H. Kelejian & Ingmar R. Prucha, 2008. "Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," CESifo Working Paper Series 2448, CESifo Group Munich.
  13. 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.
  14. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
  15. Mur, Jesús & Angulo, Ana, 2009. "Model selection strategies in a spatial setting: Some additional results," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 200-213, March.

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