Can Consumer Attitudes Forecast Tile Macroeconomy?

By Chopin, Marc C.; Darrat, Ali F. | American Economist, Spring 2000 | Go to article overview

Can Consumer Attitudes Forecast Tile Macroeconomy?


Chopin, Marc C., Darrat, Ali F., American Economist


Marc C. Chopin [*]

Ali F. Darrat [**]

I. Introduction

Researchers and policy-makers have often used surveys of consumer attitudes to forecast economic performance. The perceived importance of consumer attitudes is evidenced by the attention paid to announcements made by the Conference Board and the inclusion of the Index of Consumer Confidence (ICC) in the Commerce Department's Index of Leading Economic Indicators. However, as Katona (1978) notes, the use of consumer attitudes for forecasting is based on the assumption that "attitudes and expectations intervene between stimuli and response and they change before behavior changes." If changes in the attitudes precede changes in consumer behavior, then knowledge of these attitudes could help explain consumer spending and saving patterns [Liken and Kotler (1983), Kinsey and Collins (1994)]. However, if attitudes change after or concurrently with other movements in the economy, then measures of consumer attitudes will add little to models designed to forecast the economy. Therefore, the temporal ordering between co nsumer attitudes and their behavior should determine the value of attitudes measures in forecasting models.

Studies examining the value of consumer attitudes for forecasting economic performance have thus far failed to produce a consensus. For example, Juster and Watcher (1972), Kelly (1990), Throop (1991), and Carrol, Fubrer and Wilcox (1994) all report evidence suggesting that the ICC contains useful information for predicting consumer spending. In contrast, Hymans (1970), Lovell (1975), and Burch and Gordon (1984) contend that such measures are poor predictors of consumer spending. In spite of the controversy surrounding the predictive value of consumer attitudes, Gaski and Etzel (1986) conclude that "these surveys (measuring consumer attitudes) are still used in business planning."

We should point out that most of previous studies in this area focus on the contemporaneous correlation between some measures of consumer attitudes and economic conditions. However, in forecasting models, contemporaneous correlations are of limited usefulness. Indeed, it is now widely recognized that strong correlations between any two variables are insufficient to identify the cause and effect relationships between them. More valuable information may be found by examining movements in variables that precede changes in others. For example, if changes in consumer attitudes precede changes in consumer spending, then measures of consumer attitudes will be useful for economic and business forecasting. However, if changes in consumer spending precede changes in consumer attitudes, then measures of consumer attitudes will have no forecasting value.

Empirical work on the above issue of causality remains extremely sparse. Recently, two papers have investigated the causal relationship between indices of consumer attitudes and other economic variables, and they too report conflicting results. After testing for Granger-causality, Garner (1991) concludes that the ICC (and a similar measure published by the University of Michigan) are largely unreliable predictors of consumer spending. On the other hand, Huth, Eppright, and Taube (1994) also examine the Granger-causal relationships between four measures of consumer attitudes and various business and economic variables. In contrast to the conclusions reached by Garner, Huth et al. claim that the four measures of consumer attitudes provide useful information for forecasting changes in several measures of consumer spending, business and economic activity.

While these two studies represent an important step forward in the examination of the information content of measures of consumer attitudes, both studies appear seriously flawed. In particular, they examine the causal linkage in the context of bivariate models whose inferences are known to be potentially biased due to the "omission-of-variables" phenomenon [Lutkepohl (1982, 1993)].

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