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Simple regression‐based tests for spatial dependence

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  • Benjamin Born
  • Jörg Breitung

Abstract

We propose two simple diagnostic tests for spatial error autocorrelation and spatial lag dependence. The idea is to reformulate the testing problem such that the test statistics are asymptotically equivalent to the familiar LM test statistics. Speci cally, our version of the test is based on a simple auxiliary regression and an ordinary regression t-statistic can be used to test for spatial autocorrelation and lag dependence. We also propose a variant of the test that is robust to heteroskedasticity. This approach gives practitioners an easy to implement and robust alternative to existing tests. Monte Carlo studies show that our variants of the spatial LM tests possess comparable size and power properties even in small samples.
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Suggested Citation

  • Benjamin Born & Jörg Breitung, 2011. "Simple regression‐based tests for spatial dependence," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 330-342, July.
  • Handle: RePEc:ect:emjrnl:v:14:y:2011:i:2:p:330-342
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    References listed on IDEAS

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    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. 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.
    3. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    4. 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.
    5. Anselin, Luc, 2007. "Spatial econometrics in RSUE: Retrospect and prospect," Regional Science and Urban Economics, Elsevier, vol. 37(4), pages 450-456, July.
    6. Badi Baltagi & Dong Li, 2001. "Double Length Artificial Regressions For Testing Spatial Dependence," Econometric Reviews, Taylor & Francis Journals, vol. 20(1), pages 31-40.
    7. 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.
    8. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
    9. Kelejian, Harry H. & Robinson, Dennis P., 1998. "A suggested test for spatial autocorrelation and/or heteroskedasticity and corresponding Monte Carlo results," Regional Science and Urban Economics, Elsevier, vol. 28(4), pages 389-417, July.
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    1. repec:spr:empeco:v:55:y:2018:i:1:d:10.1007_s00181-018-1453-4 is not listed on IDEAS
    2. repec:bpj:jecome:v:8:y:2019:i:1:p:33:n:7 is not listed on IDEAS
    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. repec:liu:liucej:v:15:y:2018:i:2:p:231-248 is not listed on IDEAS
    5. Debarsy, Nicolas & Yang, Zhenlin, 2018. "Editorial for the special issue entitled: New advances in spatial econometrics: Interactions matter," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 1-5.
    6. Ming He & Kuan-Pin Lin, 2015. "Testing in a Random Effects Panel Data Model with Spatially Correlated Error Components and Spatially Lagged Dependent Variables," Econometrics, MDPI, Open Access Journal, vol. 3(4), pages 1-36, November.
    7. repec:eee:regeco:v:72:y:2018:i:c:p:35-57 is not listed on IDEAS
    8. Arnab Bhattacharjee & Eduardo Castro & João Marques, 2012. "Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal," Spatial Economic Analysis, Taylor & Francis Journals, vol. 7(1), pages 133-167, March.
    9. Astrid Pennerstorfer & Dieter Pennerstorfer, 2018. "How Small are Small Markets? Location Choice and Geographical Market Size for Child Care Services," Economics working papers 2018-14, Department of Economics, Johannes Kepler University Linz, Austria.
    10. Baltagi, Badi H. & Yang, Zhenlin, 2013. "Heteroskedasticity and non-normality robust LM tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 725-739.
    11. He, Ming & Lin, Kuan-Pin, 2015. "Testing spatial effects and random effects in a nested panel data model," Economics Letters, Elsevier, vol. 135(C), pages 85-91.
    12. Bhattacharjee, Arnab & Jensen-Butler, Chris, 2013. "Estimation of the spatial weights matrix under structural constraints," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 617-634.
    13. repec:eee:econom:v:207:y:2018:i:1:p:92-113 is not listed on IDEAS
    14. Zhenlin Yang, 2013. "LM Tests of Spatial Dependence Based on Bootstrap Critical Values," Working Papers 03-2013, Singapore Management University, School of Economics.
    15. Yang, Zhenlin, 2015. "LM tests of spatial dependence based on bootstrap critical values," Journal of Econometrics, Elsevier, vol. 185(1), pages 33-59.
    16. Badi H. Baltagi & Zhenlin Yang, 2013. "Standardized LM tests for spatial error dependence in linear or panel regressions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 103-134, February.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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