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Generalizing the Bayesian Vector Autoregression Approach for Regional Interindustry Employment Forecasting

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  • Partridge, Mark D
  • Rickman, Dan S

Abstract

The Bayesian vector autoregression (BVAR) employment-forecast approach is generalized using data for the state of Georgia. This study advances previous regional BVAR approaches by (1) incorporating regional input-output coefficients, (2) using the coefficients both to specify the prior means in one model and to weight the variances of a Minnesota-type prior in a second model, and (3) including final-demand effects and links to national and world economies. Out-of-sample forecasts produced by the generalized BVAR models are compared to forecasts produced from an autoregressive model, an unconstrained VAR model, and a Minnesota BVAR model.

Suggested Citation

  • 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.
    • Handle: RePEc:bes:jnlbes:v:16:y:1998:i:1:p:62-72
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    1. repec:rre:publsh:v:40:y:2010:i:2:p:181-96 is not listed on IDEAS
    2. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    3. Michael R. Greenberg & Michael Lahr & Nancy Mantell, 2007. "Understanding the Economic Costs and Benefits of Catastrophes and Their Aftermath: A Review and Suggestions for the U.S. Federal Government," Risk Analysis, John Wiley & Sons, vol. 27(1), pages 83-96, February.
    4. 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.
    5. 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.
    6. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41, February.
    7. Longhi, Simonetta & Nijkamp, Peter, 2006. "Forecasting regional labor market developments under spatial heterogeneity and spatial correlation," Serie Research Memoranda 0015, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    8. Simonetta Longhi & Peter Nijkamp, 2005. "Forecasting Regional Labour Market Developments Under Spatial Heterogeneity and Spatial Autocorrelation," Tinbergen Institute Discussion Papers 05-041/3, Tinbergen Institute.
    9. Maureen Kilkenny & Mark D. Partridge, 2009. "Export Sectors and Rural Development," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(4), pages 910-929.
    10. Katharina Hampel & Marcus Kunz & Norbert Schanne & Ruediger Wapler & Antje Weyh, 2006. "Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation," ERSA conference papers ersa06p196, European Regional Science Association.
    11. 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.
    12. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
    13. 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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