Brilliant, humorous, and obscure. Those words could describe two of my favorite comedians, Emo Philips* and the late Dennis Wolfberg.
They could also describe, with the addition of "exceedingly" brilliant, "scathingly" humorous, and "apparently totally" obscure, a 1957 article, "Two Important Problems in Sales Forecasting" by James H. Lorie (The Journal of Business Vol. 30, No. 3 (July 1957), pp. 172-179).
Lorie is not an unknown. When the article appeared, he was Associate Dean at the University of Chicago, School of Business. He is credited with creating the first database of stock exchange prices, allowing the type of stock analysis we take for granted today.
Yet according to Google Scholar, the article has been cited just 11 times (none since 1991), and never in any of the familiar forecasting journals or texts. I didn't find it last year while researching an article on the role of the sales force in forecasting (Foresight 35 (Fall 2014), pp. 8-13), and only came across it last week cited as a reference -- within a reference -- to Igor Gusakov's "Data-Cube Forecasting for the Forecasting Support System" (pp. 25-32 in the same issue of Foresight).
Problem 1: Combining Statistical Analysis with Subjective Judgment
Lorie first addresses the (still unresolved) challenge of "combining the wisdom of experienced businessmen with statistical analysis...in order to achieve better forecasts." (There is no mention of businesswomen, who apparently didn't exist until Peggy Olson on Mad Men.)
Lorie reviews and critiques the common statistical forecasting methods of the time: regression and correlation. (Recall that R.G. Brown's Exponential Smoothing for Predicting Demand published just the year before.) Of the former,
Perhaps a more fundamental objection to regression analysis as a means for forecasting is that it merely transforms the forecasting problem from the dependent variable to the independent variables. It requires that the analyst forecast the levels of the independent variables such as national income or industry sales rather than the level of the dependent variable, sales of a particular company's product. There is certainly very little reason to believe that forecasters have been markedly more successful in forecasting the kinds of variables which are typically considered to be independent in forecasting equations than they have been in forecasting the variables which are considered dependent.
And of the latter,
In spite of the grave limitations of correlation analysis, it will undoubtedly continue to be widely used. One of the reasons is that it is one of the very few techniques which can be readily learned by people receiving low wages and which has the comforting -- albeit superficial -- appearance of "scientific" precision.
Lorie also notes, as is now accepted in many quarters, that
...it is unreasonable to expect that more complicated massaging of numbers according to conventional statistical techniques is likely to produce very much more successful results in the future.
A similar sentiment appeared in my favorite forecasting article of the 21st century (Makridakis & Taleb, "Living in a World of Low Levels of Predictability," International Journal of Forecasting Vol. 25, No. 4 (Oct-Dec 2009), pp. 840-844):
- Statistically sophisticated, or complex, models fit past data well, but do not necessarily predict the future accurately...
- "Simple" models do not necessarily fit past data well, but predict the future better than complex or sophisticated statistical models.
We'll continue the Lorie synopsis in the next post...
*Philips is not so obscure among learned forecasters, as he was quoted in a 2013 Foresight article by Roy Batchelor: "A computer once beat me at chess. But it was no match for me at kickboxing." However, I have yet to find academic citations for Wolfberg's "The Bris" or "The Rigid Sigmoidoscopy."