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Reduced-bias estimation of spatial autoregressive models with incompletely geocoded data

Author

Listed:
  • Flavio Santi

    (University of Verona)

  • Maria Michela Dickson

    (University of Trento)

  • Diego Giuliani

    (University of Trento)

  • Giuseppe Arbia

    (Catholic University of the Sacred Heart)

  • Giuseppe Espa

    (University of Trento)

Abstract

The application of spatial Cliff–Ord models requires information about spatial coordinates of statistical units to be reliable, which is usually the case in the context of areal data. With micro-geographic point-level data, however, such information is inevitably affected by locational errors, that can be generated intentionally by the data producer for privacy protection or can be due to inaccuracy of the geocoding procedures. This unfortunate circumstance can potentially limit the use of the spatial autoregressive modelling framework for the analysis of micro data, as the presence of locational errors may have a non-negligible impact on the estimates of model parameters. This contribution aims at developing a strategy to reduce the bias and produce more reliable inference for spatial models with location errors. The proposed estimation strategy models both the spatial stochastic process and the coarsening mechanism by means of a marked point process. The model is fitted through the maximisation of a doubly-marginalised likelihood function of the marked point process, which cleans out the effects of coarsening. The validity of the proposed approach is assessed by means of a Monte Carlo simulation study under different real-case scenarios, whereas it is applied to real data on house prices.

Suggested Citation

  • Flavio Santi & Maria Michela Dickson & Diego Giuliani & Giuseppe Arbia & Giuseppe Espa, 2021. "Reduced-bias estimation of spatial autoregressive models with incompletely geocoded data," Computational Statistics, Springer, vol. 36(4), pages 2563-2590, December.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:4:d:10.1007_s00180-021-01090-7
    DOI: 10.1007/s00180-021-01090-7
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    References listed on IDEAS

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    1. Dale L. Zimmerman, 2008. "Estimating the Intensity of a Spatial Point Process from Locations Coarsened by Incomplete Geocoding," Biometrics, The International Biometric Society, vol. 64(1), pages 262-270, March.
    2. Flavio Santi & Maria Michela Dickson & Giuseppe Espa & Emanuele Taufer & Andrea Mazzitelli, 2021. "Handling spatial dependence under unknown unit locations," Spatial Economic Analysis, Taylor & Francis Journals, vol. 16(2), pages 194-216, April.
    3. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani, 2016. "Dirty spatial econometrics," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 177-189, January.
    4. James P. LeSage & R. Kelley Pace, 2014. "The Biggest Myth in Spatial Econometrics," Econometrics, MDPI, vol. 2(4), pages 1-33, December.
    5. Giuseppe Espa & Diego Giuliani & Flavio Santi & Emanuele Taufer, 2017. "Model-based variance estimation in two-dimensional systematic sampling," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 265-275, December.
    6. 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.
    7. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
    8. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    9. 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.
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