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original: Spatial aggregation and regional economic forecasting

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  • Jon R. Miller

    (Department of Economics, University of Idaho, Moscow, ID 83844-3172, USA)

Abstract

This research examines the effect of spatial aggregation on the accuracy of regional economic forecasts. Literature in econometrics, business forecasting, regional economic forecasting, spatial econometrics, and spatial time series analysis suggests that the issue is far from settled at either the theoretical or empirical levels. Univariate time series analysis is used to generate both state-level and sub-state regional forecasts of monthly total employment in the State of Idaho and its economic sub-regions. All spatial aggregation methods provide accurate forecasts, but direct state-level forecasts, and disaggregation of state-level forecasts to the sub-state level are slightly more accurate than more directly disaggregated approaches. As state-level data are often subject to less measurement error, and are also more complete and timely, this result should be encouraging to regional forecasting practitioners.

Suggested Citation

  • Jon R. Miller, 1998. "original: Spatial aggregation and regional economic forecasting," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 32(2), pages 253-266.
    • Handle: RePEc:spr:anresc:v:32:y:1998:i:2:p:253-266
    Note: Received: March 6, 1995/Accepted in revised form: June 11, 1997
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    Cited by:

    1. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    2. Gehman, Andrew & Wei, William W.S., 2020. "Optimal spatial aggregation of space–time models and applications," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    3. Su, Bin & Ang, B.W., 2010. "Input-output analysis of CO2 emissions embodied in trade: The effects of spatial aggregation," Ecological Economics, Elsevier, vol. 70(1), pages 10-18, November.
    4. Bokun, Kathryn O. & Jackson, Laura E. & Kliesen, Kevin L. & Owyang, Michael T., 2023. "FRED-SD: A real-time database for state-level data with forecasting applications," International Journal of Forecasting, Elsevier, vol. 39(1), pages 279-297.
    5. Jonathan Corcoran & Alan T. Murray & Robert J. Stimson, 2011. "Spatially Disaggregating Employment Growth Estimates," International Regional Science Review, , vol. 34(2), pages 138-156, April.
    6. Juan C Duque & Henry Laniado & Adriano Polo, 2018. "S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-25, November.

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