The Predictive Power of the Index of Consumer Sentiment

Article excerpt

THE MONTHLY RELEASE of the Index of Consumer Sentiment (ICS) by the Survey Research Center of the University of Michigan is featured in the financial press with much fanfare, especially during periods of economic uncertainty. Yet the conventional wisdom appears to be that although the index by itself has considerable predictive power, when used in conjunction with other readily available economic variables its marginal value is quite small. For example, Christopher Carroll, Jeffrey Fuhrer, and David Wilcox conclude that "consumer sentiment does indeed forecast future changes in household spending.... Further, sentiment likely has some (though probably not a great deal) of incremental predictive power relative to at least some other indicators for the growth of spending."(1) On the other hand, John Matsusaka and Argia Sbordone find evidence of a qualitatively significant causal relationship between the ICS and GDP: they estimate that between 13 and 26 percent of variations in GDP can be attributed to variations in consumer sentiment.(2)

This paper assesses the predictive power of the ICS, addressing two questions in particular. First, does the index, either alone or in conjunction with other indicator variables, sharpen predictions of recession and recovery? Second, does the index, either alone or in conjunction with other economic indicators, help to predict personal consumption expenditure?

The first question is especially timely in view of the plunge in the ICS in recent months. To answer this question, it is necessary first to define precisely and in quantitative terms what is meant by recession and what is meant by recovery, next to translate the ICS and other indicator variables into a recession signal, and finally to evaluate the accuracy of that signal as a predictor of recession. The next section summarizes the procedure used to carry out these three steps. This procedure is then applied to quarterly values of a set of indicator variables that includes the ICS as well as the spread between long- and short-term interest rates, a composite stock market index, and an index of leading indicators. This procedure is also applied to a model that generates current-quarter estimates of these indicator variables from data for the first, or first two, months of the quarter, to assess the accuracy of high-frequency predictions of recession and recovery.

Finally, the value of monthly indicator data for forecasting personal consumption expenditure is investigated. This question is motivated by the fact that monthly values of the ICS as well as of other indicator variables are available before the corresponding monthly values of personal consumption expenditure are released. An accurate and timely forecast of personal consumption expenditure and its components would be helpful in predicting periods of recession and recovery.

Predicting the Probability of Recession

Definition of Recession

A popular definition of recession is the occurrence of two or more successive quarters of decline in real GDP.(3) This definition, however, corresponds only approximately to the standard reference cycle chronology maintained by the National Bureau of Economic Research (NBER). Recession quarters as identified by the NBER coincide roughly with quarters in which real GDP declines, but the correspondence is not perfect.

A slightly more technical definition of recession that corresponds more closely with the NBER chronology is two or more successive quarters in which a weighted average of the current and immediately preceding and following quarterly GDP growth rates is negative. In particular, let [y.sub.t] denote the rate of growth of real GDP from quarter t - 1 to t, and let(4)

(1) [[bar]y.sub.t] = 0.25[y.sub.t-1] + 0.50[y.sub.t] + 0.25[y.sub.t+1].

According to the average growth rate criterion, a recession is said to begin in quarter t if that quarter is the first of two or more successive quarters for which [[bar]y. …


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