IDEAS home Printed from https://ideas.repec.org/a/sae/inrsre/v24y2001i2p226-244.html
   My bibliography  Save this article

Using Input-Output Information for Bayesian Forecasting of Industry Employment in a Regional Econometric Model

Author

Listed:
  • Dan S. Rickman

    (Department of Economics and Legal Studies, Oklahoma State University, Stillwater, rdan@okstate.edu)

Abstract

Bayesian estimation is used to incorporate regional input-output information into the employment block of a regional econometric model. The Bayesian approach used borrows both from previous work on embedding input-output information within econometric models and from Bayesian vector autoregression forecasting. The accuracy of out-of-sample forecasts produced by the alternative Bayesian models and some traditional forecast approaches are compared.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:inrsre:v:24:y:2001:i:2:p:226-244
    DOI: 10.1177/016001701761013132
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/016001701761013132
    Download Restriction: no
    File URL: https://libkey.io/10.1177/016001701761013132?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    2. LeSage, James P. & Magura, Michael, 1991. "Using interindustry input-output relations as a Bayesian prior in employment forecasting models," International Journal of Forecasting, Elsevier, vol. 7(2), pages 231-238, August.
    3. P Coomes & D Olson & D Glennon, 1991. "The Interindustry Employment Demand Variable: An Extension of the I-SAMIS Technique for Linking Input—Output and Econometric Models," Environment and Planning A, , vol. 23(7), pages 1063-1068, July.
    4. Peter W. J. Batey & Adam Z. Rose, 1990. "Extended Input-Output Models: Progress and Potential," International Regional Science Review, , vol. 13(1-2), pages 27-49, April.
    5. Hafer, R. W. & Sheehan, Richard G., 1989. "The sensitivity of VAR forecasts to alternative lag structures," International Journal of Forecasting, Elsevier, vol. 5(3), pages 399-408.
    6. Lesage, James P & Magura, Michael, 1990. "Using Bayesian Techniques for Data Pooling in Regional Payroll Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 127-135, January.
    7. Dan S. Rickman & R. Keith Schwer, 1993. "A Systematic Comparison Of The REMI and IMPLAN Models: The Case Of Southern Nevada," The Review of Regional Studies, Southern Regional Science Association, vol. 23(2), pages 143-162, Fall.
    8. L'Esperance, Wilford L. & King, Arthur E. & Sines, Richard H., 1977. "Conjoining an Ohio Input-Output Model with an Econometric Model of Ohio," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 7(1), pages 1-24.
    9. 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.
    10. Hafer, R W & Sheehan, Richard G, 1991. "Policy Inference Using VAR Models," Economic Inquiry, Western Economic Association International, vol. 29(1), pages 44-52, January.
    11. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    12. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    13. Raynauld, Jacques & Simonato, Jean-Guy, 1993. "Seasonal BVAR models : A search along some time domain priors," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 203-229.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ashkan Masouman & Charles Harvie, 2017. "Measuring Economic Change in the Illawarra, New South Wales: An Integrated Framework," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 50(3), pages 294-308, July.
    2. repec:rre:publsh:v:40:y:2010:i:2:p:181-96 is not listed on IDEAS
    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. Sergio Rey & Guy West & Mark Janikas, 2004. "Uncertainty in Integrated Regional Models," Economic Systems Research, Taylor & Francis Journals, vol. 16(3), pages 259-277.
    5. Umed Temurshoev, 2015. "Uncertainty treatment in input-output analysis," Working Papers 2015-004, Universidad Loyola Andalucía, Department of Economics.
    6. repec:rre:publsh:v:35:y:2005:i:2:p:139-60 is not listed on IDEAS
    7. Tobias Kronenberg, 2009. "Construction of Regional Input-Output Tables Using Nonsurvey Methods," International Regional Science Review, , vol. 32(1), pages 40-64, January.
    8. Hermans, Raine, . "International Mega-Trends and Growth Prospects of the Finnish Bio-technology Industry - Essays on New Economic Geography, Market Structure of the Pharmaceutical Industry, Sources of Financing, Intelle," ETLA A, The Research Institute of the Finnish Economy, number 40.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kumar, V. & Leone, Robert P. & Gaskins, John N., 1995. "Aggregate and disaggregate sector forecasting using consumer confidence measures," International Journal of Forecasting, Elsevier, vol. 11(3), pages 361-377, September.
    2. Francisco F. R. Ramos, 1996. "Forecasting market shares using VAR and BVAR models: A comparison of their forecasting performance," Econometrics 9601003, University Library of Munich, Germany.
    3. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41, February.
    4. Ribeiro Ramos, Francisco Fernando, 2003. "Forecasts of market shares from VAR and BVAR models: a comparison of their accuracy," International Journal of Forecasting, Elsevier, vol. 19(1), pages 95-110.
    5. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    6. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    7. Rangan Gupta, 2009. "Bayesian Methods Of Forecasting Inventory Investment," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 113-126, March.
    8. Anttonen, Jetro, 2018. "Nowcasting the Unemployment Rate in the EU with Seasonal BVAR and Google Search Data," ETLA Working Papers 62, The Research Institute of the Finnish Economy.
    9. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    10. Shirota, Toyoichiro, 2017. "Not All Exchange Rate Movements Are Alike : Exchange Rate Persistence and Pass-Through to Consumer Prices," Discussion paper series. A 311, Graduate School of Economics and Business Administration, Hokkaido University.
    11. Hossein Hassani & Emmanuel Sirimal Silva & Rangan Gupta & Mawuli K. Segnon, 2015. "Forecasting the price of gold," Applied Economics, Taylor & Francis Journals, vol. 47(39), pages 4141-4152, August.
    12. Rangan Gupta & Stephen Miller, 2012. "“Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(3), pages 763-782, June.
    13. Helmut Lütkepohl, 2013. "Vector autoregressive models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 6, pages 139-164, Edward Elgar Publishing.
    14. Rangan Gupta, 2012. "Forecasting House Prices for the Four Census Regions and the Aggregate US Economy: The Role of a Data-Rich Environment," Working Papers 201214, University of Pretoria, Department of Economics.
    15. Committee, Nobel Prize, 2011. "Thomas J. Sargent and Christopher A. Sims: Empirical Macroeconomics," Nobel Prize in Economics documents 2011-2, Nobel Prize Committee.
    16. Mr. Matteo Iacoviello, 2001. "Short-Term Forecasting: Projecting Italian GDPone Quarter to Two Years Ahead," IMF Working Papers 2001/109, International Monetary Fund.
    17. Auer, Simone, 2019. "Monetary policy shocks and foreign investment income: Evidence from a large Bayesian VAR," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 142-166.
    18. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    19. Pami Dua & Anirvan Banerji & Stephen M. Miller, 2006. "Performance evaluation of the New Connecticut Leading Employment Index using lead profiles and BVAR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 415-437.
    20. Phillips, Peter C. B., 1998. "Impulse response and forecast error variance asymptotics in nonstationary VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 21-56.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:inrsre:v:24:y:2001:i:2:p:226-244. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.