IDEAS home Printed from https://ideas.repec.org/a/sae/urbstu/v30y1993i10p1763-1773.html
   My bibliography  Save this article

Forecasting Provincial Business Indicator Variables and Forecast Evaluation

Author

Listed:
  • Prem P. Talwar

    (Department of Finance and Management Science, University of Alberta, 4-20K Faculty of Business Building, Edmonton, Canada, T6G 2RG)

  • Edward J. Chambers

    (Western Centre for Economic Research, University of Alberta, Canada)

Abstract

This paper evaluates a number of univariate and multivariate time-series forecasting models of selected indicator variables for three Canadian provinces: Alberta, British Columbia and Manitoba. The out of sample forecasts from these models are compared not only with themselves but with common indicators from the quarterly provincial forecast model of the Conference Board of Canada. The concepts of directional accuracy, the conditional efficiency, and the robust regression are used in evaluating the forecasts. In most cases, the strategy of combining forecasts produced superior results to those given by the Conference Board of Canada.

Suggested Citation

  • Prem P. Talwar & Edward J. Chambers, 1993. "Forecasting Provincial Business Indicator Variables and Forecast Evaluation," Urban Studies, Urban Studies Journal Limited, vol. 30(10), pages 1763-1773, December.
    • Handle: RePEc:sae:urbstu:v:30:y:1993:i:10:p:1763-1773
    DOI: 10.1080/00420989320081711
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1080/00420989320081711
    Download Restriction: no
    File URL: https://libkey.io/10.1080/00420989320081711?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. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    2. Nelson, Charles R, 1972. "The Prediction Performance of the FRB-MIT-PENN Model of the U.S. Economy," American Economic Review, American Economic Association, vol. 62(5), pages 902-917, December.
    3. Talwar, Prem P., 1993. "A simulation study of some non-parametric regression estimators," Computational Statistics & Data Analysis, Elsevier, vol. 15(3), pages 309-327, March.
    4. Stekler, H. O., 1991. "Macroeconomic forecast evaluation techniques," International Journal of Forecasting, Elsevier, vol. 7(3), pages 375-384, November.
    5. William C. Gruben & William T. Long, 1988. "The New Mexico economy: outlook for 1989," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Nov, pages 21-36.
    6. William C. Gruben & William T. Long, 1988. "Forecasting the Texas economy: applications and evaluation of a systematic multivariate time series model," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Jan, pages 11-28.
    7. Cicarelli, James, 1982. "A new method of evaluating the accuracy of economic forecasts," Journal of Macroeconomics, Elsevier, vol. 4(4), pages 469-475.
    8. Merton, Robert C, 1981. "On Market Timing and Investment Performance. I. An Equilibrium Theory of Value for Market Forecasts," The Journal of Business, University of Chicago Press, vol. 54(3), pages 363-406, July.
    9. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-590, June.
    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. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    2. Sarantis, Nicholas & Swales, Caspar, 1999. "Modelling and forecasting regional service employment in Great Britain1," Economic Modelling, Elsevier, vol. 16(3), pages 429-453, August.

    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. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    2. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    3. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    4. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
    5. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers 2010-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    6. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    7. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    8. Rangan Gupta & Moses M. Sichei, 2006. "A Bvar Model For The South African Economy," South African Journal of Economics, Economic Society of South Africa, vol. 74(3), pages 391-409, September.
    9. John Geweke & Joel Horowitz & M. Hashem Pesaran, 2006. "Econometrics: A Bird’s Eye View," CESifo Working Paper Series 1870, CESifo.
    10. Mark T. Leung & An-Sing Chen, 2005. "Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 403-420.
    11. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    12. Peter Christoffersen & Francis X. Diebold, 2002. "Financial Asset Returns, Market Timing, and Volatility Dynamics," CIRANO Working Papers 2002s-02, CIRANO.
    13. Baghestani, Hamid & Toledo, Hugo, 2017. "Do analysts' forecasts of term spread differential help predict directional change in exchange rates?," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 62-69.
    14. Peter F. Christoffersen & Francis X. Diebold, 2006. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, INFORMS, vol. 52(8), pages 1273-1287, August.
    15. Harri Pönkä, 2017. "Predicting the direction of US stock markets using industry returns," Empirical Economics, Springer, vol. 52(4), pages 1451-1480, June.
    16. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    17. Capistrán, Carlos, 2008. "Bias in Federal Reserve inflation forecasts: Is the Federal Reserve irrational or just cautious?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1415-1427, November.
    18. Busetti, Fabio & Marcucci, Juri, 2013. "Comparing forecast accuracy: A Monte Carlo investigation," International Journal of Forecasting, Elsevier, vol. 29(1), pages 13-27.
    19. Timotej Jagric, 2003. "Forecasting with leading economic indicators - a non-linear approach," Prague Economic Papers, Prague University of Economics and Business, vol. 2003(1), pages 68-83.
    20. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.

    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:urbstu:v:30:y:1993:i:10:p:1763-1773. 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: http://www.gla.ac.uk/departments/urbanstudiesjournal .

    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.