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Using Bayesian Techniques for Data Pooling in Regional Payroll Forecasting

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  • Lesage, James P
  • Magura, Michael

Abstract

This article adapts to the regional level a multicountry technique recently used by Garcia-Ferrer, Highfield, Palm, and Zellner (1987) and extended by Zellner and Hong (1987) to forecast the growth rates in gross national product across nine countries. This forecasting methodology is applied to the regional level by modeling payroll formation in seven Ohio metropolitan areas. We compare the forecasting performance of these procedures with that of a ridge estimator and find that the ridge estimator produces forecasts equal to or better than those from the newly proposed estimators. We conclude that the ridge estimator, which does not reference the pooled data information introduced by the newly proposed techniques, may serve as a benchmark against which to judge the relative importance of this kind of information in improving forecasts.

Suggested Citation

  • Lesage, James P & Magura, Michael, 1990. "Using Bayesian Techniques for Data Pooling in Regional Payroll Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 127-135, January.
    • Handle: RePEc:bes:jnlbes:v:8:y:1990:i:1:p:127-35
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    Cited by:

    1. Rickman, Dan S., 1995. "A bayesian analysis of the use of pooled coefficients in a structural regional economic model," International Journal of Forecasting, Elsevier, vol. 11(3), pages 477-490, September.
    2. 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.
    3. Kristie M. Engemann & Ruben Hernandez-Murillo & Michael T. Owyang, 2011. "Regional aggregation in forecasting: an application to the Federal Reserve’s Eighth District," Review, Federal Reserve Bank of St. Louis, vol. 93(May), pages 207-222.
    4. James LeSage & Bryce Cashell, 2015. "A comparison of vector autoregressive forecasting performance: spatial versus non-spatial Bayesian priors," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(2), pages 533-560, March.
    5. 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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