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Modelling and Diagnostics of Spatially Autocorrelated Counts

Author

Listed:
  • Robert C. Jung

    (Institut für Volkswirtschaftslehre (520K), Computational Science Lab (CSL) Hohenheim, Universität Hohenheim, 70593 Stuttgart, Germany)

  • Stephanie Glaser

    (Institut für Volkswirtschaftslehre (520K), Universität Hohenheim, 70593 Stuttgart, Germany)

Abstract

This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based on different distributional assumptions using standard numerical procedures. In addition, we advocate the use of data-coherent diagnostic tools in spatial count regression models. The application revisits a data set on the location choice of single unit start-up firms in the manufacturing industry in the US.

Suggested Citation

  • Robert C. Jung & Stephanie Glaser, 2022. "Modelling and Diagnostics of Spatially Autocorrelated Counts," Econometrics, MDPI, vol. 10(3), pages 1-17, September.
    • Handle: RePEc:gam:jecnmx:v:10:y:2022:i:3:p:31-:d:913362
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    References listed on IDEAS

    as
    1. Robert Jung & A. Tremayne, 2011. "Useful models for time series of counts or simply wrong ones?," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 59-91, March.
    2. Rebekka E. Apardian & Oleg Smirnov, 2020. "An analysis of pedestrian crashes using a spatial count data model," Papers in Regional Science, Wiley Blackwell, vol. 99(5), pages 1317-1338, October.
    3. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
    4. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    5. Isabel Proença & Ludgero Glórias, 2021. "Revisiting the Spatial Autoregressive Exponential Model for Counts and Other Nonnegative Variables, with Application to the Knowledge Production Function," Sustainability, MDPI, vol. 13(5), pages 1-22, March.
    Full references (including those not matched with items on IDEAS)

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