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A Comparison of Regional Forecasting Techniques

Author

Listed:
  • John E. Connaughton

    (UNC Charlotte)

  • Ronald A. Madsen

    (UNC Charlotte)

Abstract

The need for regional econometric forecasting models and the uses of their results has increased in recent years. A major reason for the growing interest in these models is the increasing recognition that regional economies vary in structure and performance, particularly in their cyclical behavior. Additionally, econometric models may be important for understanding differences in the trend behavior of regional economies. The purpose of this paper is to compare the quality of forecasts made using three different forecasting methods for a disaggregated regional economy.

Suggested Citation

  • John E. Connaughton & Ronald A. Madsen, 1990. "A Comparison of Regional Forecasting Techniques," The Review of Regional Studies, Southern Regional Science Association, vol. 20(3), pages 4-11, Fall.
    • Handle: RePEc:rre:publsh:v20:y:1990:i:3:p:4-11
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    References listed on IDEAS

    as
    1. Terrence Kinal & Jonathan Ratner, 1986. "A VAR Forecasting Model of a Regional Economy: Its Construction and Comparative Accuracy," International Regional Science Review, , vol. 10(2), pages 113-126, August.
    2. Dhrymes, Phoebus J. & Peristiani, Stavros C., 1988. "A comparison of the forecasting performance of WEFA and ARIMA time series methods," International Journal of Forecasting, Elsevier, vol. 4(1), pages 81-101.
    3. James G. Hoehn, 1984. "A regional economic forecasting procedure applied to Texas," Working Papers (Old Series) 8402, Federal Reserve Bank of Cleveland.
    4. Michael L. Bagshaw, 1986. "Comparison of univariate ARIMA, multivariate ARIMA and vector autoregression forecasting," Working Papers (Old Series) 8602, Federal Reserve Bank of Cleveland.
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    Cited by:

    1. J. S. Shonkwiler, 1992. "A Structural Time Series Model Of Nevada Gross Taxable Gaming Revenues," The Review of Regional Studies, Southern Regional Science Association, vol. 22(3), pages 239-249, Winter.

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