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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. 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.
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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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