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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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    Citations

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    Cited by:

    1. Seung, Chang K. & Ahn, Sung K., 2010. "Forecasting Industry Employment for a Resource-Based Economy Using Bayesian Vector Autoregressive Models," The Review of Regional Studies, Southern Regional Science Association, vol. 40(2), pages 181-196.
    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. 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.
    4. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.

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