IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v64y2011i4p345-347.html
   My bibliography  Save this article

Voss wins the Presidency! A commentary essay on "Predicting elections from biographical information about candidates: A test of the index method"

Author

Listed:
  • Voss, Kevin E.

Abstract

This comment uses the Democratic Party's nomination race from the 1924 U.S. Presidential election to develop a better understanding of Armstrong and Graefe's (2010) Biographical Index. Well-established causal indicators are necessary before actions are taken to nominate, select, or improve the standing of candidates running for election. Forecasting devices such as a Biographical Index cannot eliminate from consideration unworthy candidates. Nonetheless, the Armstrong and Graefe scale appears to have the smallest error of competing forecasting devices.

Suggested Citation

  • Voss, Kevin E., 2011. "Voss wins the Presidency! A commentary essay on "Predicting elections from biographical information about candidates: A test of the index method"," Journal of Business Research, Elsevier, vol. 64(4), pages 345-347, April.
  • Handle: RePEc:eee:jbrese:v:64:y:2011:i:4:p:345-347
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148-2963(10)00158-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Armstrong, J. Scott & Graefe, Andreas, 2009. "Predicting Elections from Biographical Information about Candidates," MPRA Paper 16461, University Library of Munich, Germany.
    2. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
    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. Graefe, Andreas & Armstrong, J. Scott, 2011. "Conditions under which index models are useful: Reply to bio-index commentaries," Journal of Business Research, Elsevier, vol. 64(7), pages 693-695, July.

    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. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    2. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    3. Amare Tesfaw & Feyera Senbeta & Dawit Alemu & Ermias Teferi, 2021. "Value Chain Analysis of Eucalyptus Wood Products in the Blue Nile Highlands of Northwestern Ethiopia," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    4. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    5. Sbrana, Giacomo & Silvestrini, Andrea, 2014. "Random switching exponential smoothing and inventory forecasting," International Journal of Production Economics, Elsevier, vol. 156(C), pages 283-294.
    6. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    7. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844.
    8. Robert Fildes & Paul Goodwin, 2007. "Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting," Interfaces, INFORMS, vol. 37(6), pages 570-576, December.
    9. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    10. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
    11. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    12. Green, Kesten C. & Armstrong, J. Scott, 2007. "Structured analogies for forecasting," International Journal of Forecasting, Elsevier, vol. 23(3), pages 365-376.
    13. Zeng, Michael A., 2018. "Foresight by online communities – The case of renewable energies," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 27-42.
    14. Hloušková, Z. & Lekešová, M. & Slížka, E., 2014. "Microsimulation Model Estimating Czech Farm Income from Farm Accountancy Data Network Database," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(3), pages 1-11, September.
    15. Alvarado-Valencia, Jorge & Barrero, Lope H. & Önkal, Dilek & Dennerlein, Jack T., 2017. "Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(1), pages 298-313.
    16. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    17. Bera, Soumitra Kumar, 2010. "Forecasting model of small scale industrial sector of West Bengal," MPRA Paper 28144, University Library of Munich, Germany.
    18. Armstrong, J. Scott & Fildes, Robert, 2006. "Making progress in forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 433-441.
    19. Sbrana, Giacomo & Silvestrini, Andrea, 2019. "Random switching exponential smoothing: A new estimation approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 211-220.
    20. Giacomo Sbrana, 2010. "Forecasting damped trend exponential smoothing: an algebraic viewpoint," Working Papers 10-08, Association Française de Cliométrie (AFC).

    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:eee:jbrese:v:64:y:2011:i:4:p:345-347. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    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.