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Artificial regression test diagnostics for impact measures in spatial models

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  • Deng, Mingyu
  • Wang, Mingxi

Abstract

This paper derives two test statistics based on Outer-Product Gradient method and Double-Length Regression for testing spatial impact measures. Both are computationally simple. Their Monte Carlo performance becomes better as the sample size gets larger.

Suggested Citation

  • Deng, Mingyu & Wang, Mingxi, 2022. "Artificial regression test diagnostics for impact measures in spatial models," Economics Letters, Elsevier, vol. 217(C).
  • Handle: RePEc:eee:ecolet:v:217:y:2022:i:c:s0165176522002336
    DOI: 10.1016/j.econlet.2022.110689
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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. James P. LeSage & Manfred M. Fischer, 2012. "Estimates of the Impact of Static and Dynamic Knowledge Spillovers on Regional Factor Productivity," International Regional Science Review, , vol. 35(1), pages 103-127, January.
    3. 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.
    4. 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.
    5. Benjamin Born & Jörg Breitung, 2011. "Simple regression‐based tests for spatial dependence," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 330-342, July.
    6. Giuseppe Arbia & Anil K. Bera & Osman Doğan & Süleyman Taşpınar, 2020. "Testing Impact Measures in Spatial Autoregressive Models," International Regional Science Review, , vol. 43(1-2), pages 40-75, January.
    7. 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.
    8. Suwanprasert, Wisarut, 2022. "The international spillover effects of US trade policy uncertainty," Economics Letters, Elsevier, vol. 212(C).
    9. 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.
    10. Süleyman Taşpınar & Osman Doğan & Wim P. M. Vijverberg, 2018. "GMM inference in spatial autoregressive models," Econometric Reviews, Taylor & Francis Journals, vol. 37(9), pages 931-954, October.
    11. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    12. Badi Baltagi & Dong Li, 2001. "Double Length Artificial Regressions For Testing Spatial Dependence," Econometric Reviews, Taylor & Francis Journals, vol. 20(1), pages 31-40.
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    More about this item

    Keywords

    Spatial impact measure; Artificial regression; Double-Length Regression; Outer-Product Gradient;
    All these keywords.

    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
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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