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An Evaluation of Alternative Strategies for Incorporating Interindustry Relationships into a Regional Employment Forecasting Model

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  • Rickman, Dan S.

    (Oklahoma State University)

  • Miller, Steven R.

    (Oklahoma State University)

Abstract

Alternative strategies for incorporating interindustry relationships into industry employment forecasting equations are evaluated. Strategies that are based on using input-output information to select which industry employment variables to include as independent variables in each equation are compared to those of including input-output-based aggregate demand variables. The strategies also differ according to the degree of endogeneity between industry employment levels, whether model selection procedures are used, and whether restrictions are placed upon the coefficients during estimation. Relative forecast performance of the alternative models and an examination of the employment multipliers serve as criteria in the evaluation of the models.

Suggested Citation

  • Rickman, Dan S. & Miller, Steven R., 2002. "An Evaluation of Alternative Strategies for Incorporating Interindustry Relationships into a Regional Employment Forecasting Model," The Review of Regional Studies, Southern Regional Science Association, vol. 32(1), pages 133-147, Winter/Sp.
    • Handle: RePEc:rre:publsh:v:32:y:2002:i:1:p:133-147
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    References listed on IDEAS

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    1. Dan S. Rickman, 2001. "Using Input-Output Information for Bayesian Forecasting of Industry Employment in a Regional Econometric Model," International Regional Science Review, , vol. 24(2), pages 226-244, April.
    2. LeSage, James P. & Magura, Michael, 1991. "Using interindustry input-output relations as a Bayesian prior in employment forecasting models," International Journal of Forecasting, Elsevier, vol. 7(2), pages 231-238, August.
    3. Partridge, Mark D & Rickman, Dan S, 1998. "Generalizing the Bayesian Vector Autoregression Approach for Regional Interindustry Employment Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 62-72, January.
    4. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    5. Sergio J. Rey, 1998. "The Performance of Alternative Integration Strategies for Combining Regional Econometric and Input-Output Models," International Regional Science Review, , vol. 21(1), pages 1-35, April.
    6. Jeff B. Cromwell & Michael J. Hannan, 1993. "The Utility of Impulse Response Functions in Regional Analysis: Some Critical Issues," International Regional Science Review, , vol. 15(2), pages 199-222, August.
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    Cited by:

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    3. Dan S. Rickman & Steven R. Miller & Russell McKenzie, 2009. "Spatial and sectoral linkages in regional models: A Bayesian vector autoregression forecast evaluation," Papers in Regional Science, Wiley Blackwell, vol. 88(1), pages 29-41, March.
    4. James P. LeSage & Daniel Hendrikz, 2019. "Large Bayesian vector autoregressive forecasting for regions: A comparison of methods based on alternative disturbance structures," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(3), pages 563-599, June.

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