Illusions in regression analysis

International Journal of Forecasting

Volumn 28, Issue 3, 2012, Pages 689-694

  Armstrong, J Scott ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84862535656     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijforecast.2012.02.001     Document Type: Editorial

References (39)

1. Allen P.G., Fildes R. Econometric forecasting. Principles of forecasting 2001, Kluwer Academic Publishers, Boston. J.S. Armstrong (Ed.).
2. Armstrong, J. S. (1968a). Long-range forecasting for a consumer durable in an international market. Ph.D. Thesis. MIT.
3. Armstrong J.S. Long-range forecasting for international markets: the use of causal models. Marketing and the new science of planning 1968, American Marketing Association, Chicago, Available at: http://goo.gl/oKYUH. R.L. King (Ed.).
4. Armstrong J.S. How to avoid exploratory research. Journal of Advertising Research 1970, 10(4):27-30.
5. Armstrong J.S. Long-range forecasting 1985, John Wiley, New York.
6. Armstrong J.S. Judgmental bootstrapping: inferring experts' rules for forecasting. Principles of forecasting 2001, Kluwer Academic Publishers, Boston. J.S. Armstrong (Ed.).
7. Armstrong J.S. Evidence-based advertising: an application to persuasion. International Journal of Advertising 2011, 30(5):743-767.
8. Armstrong J.S., Collopy F. Error measures for generalizing about forecasting methods: empirical comparisons. International Journal of Forecasting 1992, 8:69-80.
9. Armstrong J.S., Graefe A. Predicting elections from biographical information about candidates: a test of the index method. Journal of Business Research 2011, 64:699-706.
10. Armstrong J.S., Patnaik S. Using quasi-experimental data to develop principles for persuasive advertising. Journal of Advertising Research 2009, 49(2):170-175.
11. Christ C.F. Simultaneous equation estimation: any verdict yet?. Econometrica 1960, 28:835-845.
12. Cumming G. Understanding the new statistics: effect sizes, confidence intervals and meta-analysis 2012, Routledge, New York.
13. Dana J., Dawes R.M. The superiority of simple alternatives to regression for social science predictions. Journal of Educational and Behavioral Statistics 2004, 29(3):317-331.
14. Einhorn H.J. Alchemy in the behavioral sciences. Public Opinion Quarterly 1972, 36:367-378.
15. Einhorn H.J., Hogarth R. Unit weighting schemes for decision making. Organizational Behavior and Human Performance 1975, 13:171-192.
16. Fildes R., Makridakis S. The impact of empirical accuracy papers on time series analysis and forecasting. International Statistical Review 1995, 63:289-308.
17. Freedman D.A. Statistical models and shoe leather. Sociological Methodology 1991, 21:291-313.
18. Friedman M.A., Schwartz A.J. Alternative approaches to analyzing economic data. American Economic Review 1991, 81:39-49.
19. García-Ferrer A. In memory of Arnold Zellner, a great person and scientist. International Journal of Forecasting 2011, 27:961-967.
20. Goldstein D.G., Gigerenzer G. Fast and frugal forecasting. International Journal of Forecasting 2009, 25:760-772.
21. Graefe, A., Armstrong, J. S., Cuzán, A. G., & Jones, R. J. Jr. (2012). Combining Forecasts: An Application to Election Forecasts. Working Paper.
22. Grove W.M., Meehl P.E. Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: the clinical-statistical controversy. Psychology, Public Policy, and Law 1996, 2:293-323.
23. Hauer E. The harm done by tests of significance. Accident Analysis and Prevention 2004, 36:495-500.
24. Hogarth, R. M. (in press). When simple is hard to accept. In P. M. Todd & G. Gigerenzer (Eds.), Ecological rationality: intelligence in the world. Oxford: Oxford University Press.
25. Ioannidis J.P.A. Why most published research findings are false. PLoS Medicine 2005, 2:696-701.
26. Ioannidis J.P.A. Why most discovered true associations are inflated. Epidemiology 2008, 19:640-648.
27. Karni E., Shapiro B.K. Tales of horror from ivory towers. Journal of Political Economy 1980, 88(1):210-212.
28. Kennedy P. Sinning in the basement: what are the rules? the ten commandments of applied econometrics. Journal of Economic Surveys 2002, 16:569-589.
29. Meehl P.E. Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology 1978, 46:806-834.
30. Ord K. The illusion of predictability: a call to action. International Journal of Forecasting 2012, 28(3):717-718.
31. Pant P.N., Starbuck W.H. Innocents in the forest: forecasting and research methods. Journal of Management 1990, 16:433-446.
32. Schmidt F.L. The relative efficiency of regression and simple unit predictor weights in applied differential psychology. Educational and Psychological Measurement 1971, 31:699-714.
33. Schmidt F.L. Statistical significance testing and cumulative knowledge in psychology: implications for training of researchers. Psychological Methods 1996, 1:115-129.
34. Soyer E., Hogarth R. The Illusion of predictability: how regression statistics mislead experts. International Journal of Forecasting 2012, 28(3):695-711.
35. Sparks J. The works of Benjamin Franklin 1844, Charles Tappan Publisher, Boston.
36. Wold H., Jureen L. Demand analysis 1953, John Wiley, New York.
37. Zellner A. Keep it sophisticatedly simple. Simplicity, inference, and modelling: keeping it sophisticatedly simple 2001, Cambridge University Press, Cambridge. H. Keuzenkamp, M. McAleer (Eds.).
38. Ziliak S.T. Visualizing uncertainty: on Soyer's and Hogarth's "The illusion of predictability: how regression statistics mislead experts". International Journal of Forecasting 2012, 28(3):712-714.
39. Ziliak S.T., McCloskey D.N. The cult of statistical significance: how the standard error costs us jobs, justice, and lives 2008, The University of Michigan Press, Ann Arbor.