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A Bayesian forecasting approach to constructing regional input-output based employment multipliers

Author

Listed:
  • Dan S. Rickman

    (Department of Economics, College of Business, Oklahoma State University, Stillwater, OK 74078, USA)

Abstract

A Bayesian mixed estimation framework is used to examine the forecast accuracy of alternative closures of an input-output model for the Oklahoma economy. The closures correspond to textbook Type I and Type II multipliers, as well as variations of extended input-output and Type IV multipliers. Relative forecast performance of the alternative IO model closures determines which set of multipliers should be used for impact analysis. The exercise reveals differences in forecast accuracy across alternative IO model closures, suggesting that before closures of a particular IO model are adopted, they should be tested for accuracy in predicting the time series data for the regional economy under scrutiny.

Suggested Citation

  • Dan S. Rickman, 2002. "A Bayesian forecasting approach to constructing regional input-output based employment multipliers," Papers in Regional Science, Springer;Regional Science Association International, vol. 81(4), pages 483-498.
    • Handle: RePEc:spr:presci:v:81:y:2002:i:4:p:483-498
    Note: Received: 26 November 2000
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    Citations

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    Cited by:

    1. Ashkan Masouman & Charles Harvie, 2020. "Forecasting, impact analysis and uncertainty propagation in regional integrated models: A case study of Australia," Environment and Planning B, , vol. 47(1), pages 65-83, January.
    2. Oriane Lafuente-Sampietro, 2021. "The multiplier effect of convertible local currencies : case study on two French schemes," Working Papers halshs-03324625, HAL.
    3. 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.
    4. Mark Partridge & Dan Rickman, 2010. "Computable General Equilibrium (CGE) Modelling for Regional Economic Development Analysis," Regional Studies, Taylor & Francis Journals, vol. 44(10), pages 1311-1328.
    5. 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.
    6. Rickman, Dan & Wang, Hongbo, 2020. "Assessing State Economic Development from Motion Picture and Television Production Incentives: Standardizing the Industry for Analysis," MPRA Paper 104052, University Library of Munich, Germany.
    7. Malcolm Beynon & Max Munday, 2008. "Stochastic key sector analysis: an application to a regional input–output framework," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(4), pages 863-877, December.
    8. Andrew J. Cassey & David W. Holland & Abdul Razack, 2011. "Comparing the Economic Impact of an Export Shock in Two Modeling Frameworks," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(4), pages 623-638.
    9. repec:rre:publsh:v:35:y:2005:i:2:p:139-60 is not listed on IDEAS
    10. 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.
    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.

    More about this item

    Keywords

    Input-output; Bayesian forecasting; IMPLAN; regional multipliers;
    All these keywords.

    JEL classification:

    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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