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The Predictive Power of the Index of Consumer Sentiment

From: Brookings Papers on Economic Activity
2001, 1
pp. 175-216 | 10.1353/eca.2001.0010

In lieu of an abstract, here is a brief excerpt of the content:

Comment and Discussion

Michael C. Lovell: George Katona developed the Index of Consumer Sentiment at the University of Michigan some fifty years ago. Today we are all indebted to Philip Howrey for continuing the great University of Michigan tradition with a fine paper that provides useful information about forecasting business cycles.

Two things about the ICS deserve particular notice. First, Katona did not create the ICS for forecasting purposes, or even to elicit useful information. When he was developing the Survey of Consumer Finances for the Federal Reserve, Katona inserted the five attitudinal questions from which the ICS is calculated in order to loosen up the respondents, so they would be more forthcoming about their income and other personal financial details.1 Fortunately, Katona tallied the results.

The other interesting—and surprising—thing about the ICS is that, after fifty years, it is still with us. Thirty years ago the ICS was regarded as a relic of the past, an anachronism. Its place on the evening news had been stolen by the forecasts by Lawrence Klein at Wharton Econometrics, by Otto Eckstein at Data Resources Incorporated, and by Michael Evans at Chace Econometrics. Today, however, the findings of the econometric models no longer make it onto the evening news or the front pages of the financial press, and the ICS is stronger than ever in the public's eye. A recent Internet search for "consumer sentiment" produced 46,800 hits. It is fair to say that many regard the ICS as the best one-eyed monster in the valley of the blind. If we believe in the survival of the fittest, we must conclude that consumers know something the econometric forecasters do not. [End Page 208]

Let us look at how Howrey proceeds to size up the predictive power of the ICS. An advantage that Phil has over earlier investigators is that, with the passage of time, more observations have accumulated: the paper covers about 160 quarters. That is a lot of data, which help generate results that achieve significance at customary levels. On the other hand, over that forty-year period there have been only six recessions, or six opportunities for a forecaster to hit or miss the peak of the business cycle. Of course, one can also make false predictions of recession—a type II error, so to speak. For example, in 1965-66 there was a sharp decline in the ICS of 16.2 percent (top panel of Howrey's figure 1), only slightly less than the recent decline of 19.4 percent that has so spooked the stock market. Yet that decline in the mid-1960s turned out to be a false alarm.

The quadratic probability score (QPS) that Howrey uses to evaluate forecasts is designed to penalize both missed turns and false signals. It is a symmetric index in that it treats both as equally serious. It takes as its input probability statements about the likelihood of recession in each quarter and compares them with what actually happened. A lazy or naïve forecaster might always make the same probability prediction for every quarter, or one could use what happened last time. Howrey finds that the ICS does better than the naïve forecaster, but so do several other indicators.

I found table 2 of Howrey's paper almost overwhelming. He runs the ICS in a race with three other indicators: an interest rate spread (the difference between the ten-year Treasury note and the three-month Treasury bill rate, whose predictive power is related to an inverted Treasury yield curve), the New York Stock Exchange composite price index, and the Conference Board's index of leading economic indicators. He exhaustively considers sixteen alternative autoregressive (AR) models. The first includes none of the indicators; the next four look at the alternative indicators one at a time; the next six consider all possible pairs of the indicators; the next four consider all possible combinations involving three of the four candidate variables; and the last model includes all four candidates. This may seem a bit much, but I much prefer exhaustive to selective reporting, and it allows us to look for results that are...