IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/81663.html
   My bibliography  Save this paper

Illusions in Regression Analysis

Author

Listed:
  • Armstrong, J. Scott

Abstract

Soyer and Hogarth’s article, “The Illusion of Predictability,” shows that diagnostic statistics that are commonly provided with regression analysis lead to confusion, reduced accuracy, and overconfidence. Even highly competent researchers are subject to these problems. This overview examines the Soyer-Hogarth findings in light of prior research on illusions associated with regression analysis. It also summarizes solutions that have been proposed over the past century. These solutions would enhance the value of regression analysis.

Suggested Citation

  • Armstrong, J. Scott, 2011. "Illusions in Regression Analysis," MPRA Paper 81663, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:81663
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/81663/1/MPRA_paper_81663.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    2. repec:bla:jecsur:v:16:y:2002:i:4:p:569-89 is not listed on IDEAS
    3. Armstrong, J. Scott, 1970. "How to avoid exploratory research," MPRA Paper 81666, University Library of Munich, Germany.
    4. Peter E. Kennedy, 2002. "Sinning in the Basement: What are the Rules? The Ten Commandments of Applied Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 16(4), pages 569-589, September.
    5. Karni, Edi & Shapiro, Barbara, 1980. "Tales of Horror from Ivory Towers," Journal of Political Economy, University of Chicago Press, vol. 88(1), pages 210-212, February.
    6. Armstrong, J. Scott & Graefe, Andreas, 2011. "Predicting elections from biographical information about candidates: A test of the index method," Journal of Business Research, Elsevier, vol. 64(7), pages 699-706, July.
    7. Soyer, Emre & Hogarth, Robin M., 2012. "The illusion of predictability: How regression statistics mislead experts," International Journal of Forecasting, Elsevier, vol. 28(3), pages 695-711.
    8. Goldstein, Daniel G. & Gigerenzer, Gerd, 2009. "Fast and frugal forecasting," International Journal of Forecasting, Elsevier, vol. 25(4), pages 760-772, October.
    9. Jason Dana & Robyn M. Dawes, 2004. "The Superiority of Simple Alternatives to Regression for Social Science Predictions," Journal of Educational and Behavioral Statistics, , vol. 29(3), pages 317-331, September.
    10. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    11. Friedman, Milton & Schwartz, Anna J, 1991. "Alternative Approaches to Analyzing Economic Data," American Economic Review, American Economic Association, vol. 81(1), pages 39-49, March.
    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. Cogoljević, Dušan & Gavrilović, Milan & Roganović, Miloš & Matić, Ivana & Piljan, Ivan, 2018. "Analyzing of consumer price index influence on inflation by multiple linear regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 941-944.

    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. 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.
    2. Woike, Jan K. & Hoffrage, Ulrich & Petty, Jeffrey S., 2015. "Picking profitable investments: The success of equal weighting in simulated venture capitalist decision making," Journal of Business Research, Elsevier, vol. 68(8), pages 1705-1716.
    3. Andreas Graefe & Kesten C Green & J Scott Armstrong, 2019. "Accuracy gains from conservative forecasting: Tests using variations of 19 econometric models to predict 154 elections in 10 countries," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-14, January.
    4. Katsikopoulos, Konstantinos V. & Şimşek, Özgür & Buckmann, Marcus & Gigerenzer, Gerd, 2022. "Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?," International Journal of Forecasting, Elsevier, vol. 38(2), pages 613-619.
    5. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
    6. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    7. J. Scott Armstrong & Kesten C. Green, 2005. "Demand Forecasting: Evidence-based Methods," Monash Econometrics and Business Statistics Working Papers 24/05, Monash University, Department of Econometrics and Business Statistics.
    8. Kim, Jae H. & Ji, Philip Inyeob, 2015. "Significance testing in empirical finance: A critical review and assessment," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 1-14.
    9. Green, Kesten C. & Armstrong, J. Scott & Graefe, Andreas, 2015. "Golden rule of forecasting rearticulated: Forecast unto others as you would have them forecast unto you," Journal of Business Research, Elsevier, vol. 68(8), pages 1768-1771.
    10. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    11. George S. Tavlas, 2015. "In Old Chicago: Simons, Friedman, and the Development of Monetary‐Policy Rules," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(1), pages 99-121, February.
    12. Jyotirmoy Sarkar, 2018. "Will P†Value Triumph over Abuses and Attacks?," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(4), pages 66-71, July.
    13. Kumar, V. & Sunder, Sarang & Sharma, Amalesh, 2015. "Leveraging Distribution to Maximize Firm Performance in Emerging Markets," Journal of Retailing, Elsevier, vol. 91(4), pages 627-643.
    14. Hu, Xincheng & Banks, Jonathan & Wu, Linping & Liu, Wei Victor, 2020. "Numerical modeling of a coaxial borehole heat exchanger to exploit geothermal energy from abandoned petroleum wells in Hinton, Alberta," Renewable Energy, Elsevier, vol. 148(C), pages 1110-1123.
    15. P.A.V.B. Swamy & Jatinder S. Mehta & I-Lok Chang, 2017. "Endogeneity, Time-Varying Coefficients, and Incorrect vs. Correct Ways of Specifying the Error Terms of Econometric Models," Econometrics, MDPI, vol. 5(1), pages 1-17, February.
    16. Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
    17. Riccardo Viale, 2021. "The epistemic uncertainty of COVID-19: failures and successes of heuristics in clinical decision-making," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 20(1), pages 149-154, June.
    18. Paroissien, Emmanuel, 2020. "Forecasting bulk prices of Bordeaux wines using leading indicators," International Journal of Forecasting, Elsevier, vol. 36(2), pages 292-309.
    19. Kevin J. Boyle & Mark Morrison & Darla Hatton MacDonald & Roderick Duncan & John Rose, 2016. "Investigating Internet and Mail Implementation of Stated-Preference Surveys While Controlling for Differences in Sample Frames," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 64(3), pages 401-419, July.
    20. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.

    More about this item

    Keywords

    a priori analysis; decision-making; ex ante testing; forecasting; non-experimental data; statistical significance; uncertainty;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    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:pra:mprapa:81663. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.