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Dirty spatial econometrics

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  • Giuseppe Arbia
  • Giuseppe Espa
  • Diego Giuliani

Abstract

Spatial data are often contaminated with a series of imperfections that reduce their quality and can dramatically distort the inferential conclusions based on spatial econometric modeling. A ÒcleanÓ ideal situation considered in standard spatial econometrics textbooks is when we fit Cliff-Ord-type models to data where the spatial units constitute the full population, there are no missing data and there is no uncertainty on the spatial observations that are free from measurement and locational errors. Unfortunately in practical cases the reality is often very different and the datasets contain all sorts of imperfections: they are often based on a sample drawn from the whole population, some data are missing and they almost invariably contain both attribute and locational errors. This is a situation of ÒdirtyÓ spatial econometric modelling. Through a series of Monte Carlo experiments, this paper considers the effects on spatial econometric model estimation and hypothesis testing of two specific sources of dirt, namely missing data and locational errors.

Suggested Citation

  • Giuseppe Arbia & Giuseppe Espa & Diego Giuliani, 2015. "Dirty spatial econometrics," DEM Discussion Papers 2015/09, Department of Economics and Management.
    • Handle: RePEc:trn:utwpem:2015/09
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    1. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    2. Eva Deuchert & Conny Wunsch, 2014. "Evaluating nationwide health interventions: Malawi's insecticide-treated-net distribution programme," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 523-552, February.
    3. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    4. Baltagi, Badi H. & Egger, Peter & Pfaffermayr, Michael, 2007. "Estimating models of complex FDI: Are there third-country effects?," Journal of Econometrics, Elsevier, vol. 140(1), pages 260-281, September.
    5. Dubin, Robin A., 1992. "Spatial autocorrelation and neighborhood quality," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 433-452, September.
    6. D A Griffith & R J Bennett & R P Haining, 1989. "Statistical Analysis of Spatial Data in the Presence of Missing Observations: A Methodological Guide and an Application to Urban Census Data," Environment and Planning A, , vol. 21(11), pages 1511-1523, November.
    7. Harry Kelejian & Ingmar Prucha, 2010. "Spatial models with spatially lagged dependent variables and incomplete data," Journal of Geographical Systems, Springer, vol. 12(3), pages 241-257, September.
    8. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    9. Alfonso Flores‐Lagunes & Kurt Erik Schnier, 2012. "Estimation of sample selection models with spatial dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 173-204, March.
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    Cited by:

    1. 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.
    2. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani, 2015. "Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality," Econometrics, MDPI, vol. 3(4), pages 1-10, October.
    3. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani & Maria Michela Dickson, 2017. "Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(2-3), pages 326-346, July.
    4. Edoardo Baldoni & Roberto Esposti, 2021. "Agricultural Productivity in Space: an Econometric Assessment Based on Farm‐Level Data," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(4), pages 1525-1544, August.
    5. Takahisa Yokoi, 2018. "Spatial lag dependence in the presence of missing observations," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(1), pages 25-40, January.

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    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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