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Evidence of spurious results along with spatially autocorrelated errors in the context of geographically weighted regression for two independent SAR(1) processes

Author

Listed:
  • Christos Agiakloglou

    (University of Piraeus)

  • Cleon Tsimbos

    (University of Piraeus)

  • Apostolos Tsimpanos

    (University of the Aegean)

Abstract

The aim of this paper is to examine the performance of the geographically weighted regression (GWR) technique in terms of recognizing linear relationships at the local level. Using a Monte Carlo analysis with two independent spatially autocorrelated SAR(1) processes, this study finds that spurious behavior exists at the local level along with spatially autocorrelated errors. Therefore, GWR estimation results must be interpreted with caution.

Suggested Citation

  • Christos Agiakloglou & Cleon Tsimbos & Apostolos Tsimpanos, 2019. "Evidence of spurious results along with spatially autocorrelated errors in the context of geographically weighted regression for two independent SAR(1) processes," Empirical Economics, Springer, vol. 57(5), pages 1613-1631, November.
    • Handle: RePEc:spr:empeco:v:57:y:2019:i:5:d:10.1007_s00181-018-1510-z
    DOI: 10.1007/s00181-018-1510-z
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    References listed on IDEAS

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    1. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    2. David C Wheeler, 2009. "Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso," Environment and Planning A, , vol. 41(3), pages 722-742, March.
    3. Granger, Clive W.J. & Hyung, Namwon & Jeon, Yongil, 1998. "Spurious Regressions with Stationary Series," University of California at San Diego, Economics Working Paper Series qt7r3353t8, Department of Economics, UC San Diego.
    4. A. Stewart Fotheringham & Taylor M. Oshan, 2016. "Geographically weighted regression and multicollinearity: dispelling the myth," Journal of Geographical Systems, Springer, vol. 18(4), pages 303-329, October.
    5. David Wheeler & Catherine Calder, 2007. "An assessment of coefficient accuracy in linear regression models with spatially varying coefficients," Journal of Geographical Systems, Springer, vol. 9(2), pages 145-166, June.
    6. Manfred M. Fischer & Arthur Getis (ed.), 2010. "Handbook of Applied Spatial Analysis," Springer Books, Springer, number 978-3-642-03647-7, March.
    7. Antonio Páez & Steven Farber & David Wheeler, 2011. "A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships," Environment and Planning A, , vol. 43(12), pages 2992-3010, December.
    8. Clive Granger & Namwon Hyung & Yongil Jeon, 2001. "Spurious regressions with stationary series," Applied Economics, Taylor & Francis Journals, vol. 33(7), pages 899-904.
    9. David C Wheeler, 2007. "Diagnostic Tools and a Remedial Method for Collinearity in Geographically Weighted Regression," Environment and Planning A, , vol. 39(10), pages 2464-2481, October.
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    11. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
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    More about this item

    Keywords

    Geographically weighted regression; Spurious regression; Stationary spatial autoregressive SAR(1) processes; Spatially autocorrelated errors;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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