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Forecasting industrial employment figures in Southern California: A Bayesian vector autoregressive model

Author

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  • Anil Puri

    (School of Business Administration and Economics, California State University, Fullerton, Fullerton, CA 92834, USA)

  • Gökçe Soydemir

    (College of Business Administration, University of Texas-Pan American, Edinburg, TX 78539, USA)

Abstract

In this paper, we construct a Bayesian vector autoregressive model to forecast the industrial employment figures of the Southern California economy. The model includes both national and state variables. The root mean squared error (RMSE) and the Theil's U statistics are used in selecting the Bayesian prior. The out-of-sample forecasts derived from each model and prediction of the turning points show that the Bayesian VAR model outperforms the ARIMA and the unrestricted VAR models. At longer horizons the BVAR model appears to do relatively better than alternative models. A prior that becomes increasingly looser produces more accurate forecasts than a tighter prior in the BVAR estimations.

Suggested Citation

  • Anil Puri & Gökçe Soydemir, 2000. "Forecasting industrial employment figures in Southern California: A Bayesian vector autoregressive model," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 34(4), pages 503-514.
    • Handle: RePEc:spr:anresc:v:34:y:2000:i:4:p:503-514
    Note: Received: March 1999/Accepted: November 1999
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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. repec:rre:publsh:v:40:y:2010:i:2:p:181-96 is not listed on IDEAS

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