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Forecasting the Local Economy, Using Time-Series and Shift—Share Techniques

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  • J A Kurre
  • B R Weller

Abstract

Although shift – share is an inexpensive and easy means of making regional forecasts, its accuracy depends on the method used in forecasting the competitive component of the region. As theory is unclear as to the sign, let alone the magnitude, of the competitive component of the next period, it would be appropriate to use time-series techniques to forecast it. Unfortunately the typical method of application of shift–share results in too few data points for the application of time-series analysis. In this paper an alternative method of calculating the competitive component that resolves that problem is presented. In an empirical test of the technique, time-series methods are used to forecast the competitive series for a metropolitan region. The results are found to compare favorably with the forecasts of other techniques.

Suggested Citation

  • J A Kurre & B R Weller, 1989. "Forecasting the Local Economy, Using Time-Series and Shift—Share Techniques," Environment and Planning A, , vol. 21(6), pages 753-770, June.
    • Handle: RePEc:sae:envira:v:21:y:1989:i:6:p:753-770
    DOI: 10.1068/a210753
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    References listed on IDEAS

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    1. Weller, Barry R & Kurre, James A, 1987. "Applicability of the Transfer Function Approach to Forecasting Employment Levels in Small Regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 21(1), pages 34-43, March.
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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. Ciro Biderman & Bruno Hermann & Fernando Cotelo, 2017. "Economic impacts of São Paulo downtown rehabilitation: a simplified approach with micro-foundations," Regional Studies, Taylor & Francis Journals, vol. 51(9), pages 1362-1373, September.
    3. Matias Mayor & Ana Jesus Lopez & Rigoberto Perez, 2007. "Forecasting Regional Employment with Shift-Share and ARIMA Modelling," Regional Studies, Taylor & Francis Journals, vol. 41(4), pages 543-551.
    4. Dogru, Tarik & Sirakaya-Turk, Ercan, 2017. "Engines of tourism's growth: An examination of efficacy of shift-share regression analysis in South Carolina," Tourism Management, Elsevier, vol. 58(C), pages 205-214.

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