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

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Author Info

  • 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.

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Bibliographic Info

Article provided by Springer in its journal Papers in Regional Science.

Volume (Year): 81 (2002)
Issue (Month): 4 ()
Pages: 483-498

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Handle: RePEc:spr:presci:v:81:y:2002:i:4:p:483-498

Note: Received: 26 November 2000
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Related research

Keywords: Input-output; Bayesian forecasting; IMPLAN; regional multipliers;

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Malcolm Beynon & Max Munday, 2008. "Stochastic key sector analysis: an application to a regional input–output framework," The Annals of Regional Science, Springer, vol. 42(4), pages 863-877, December.

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