TV weather forecast or look through the. window? Expert and consumer. expectations about macroeconomic

Transcription

1 TV weather forecast or look through the window? Expert and consumer expectations about macroeconomic conditions Michael W. M. Roos Abstract This paper investigates the empirical relation between consumer and expert expectations about macroeconomic conditions in Germany. Using data from the EU Consumer Con dence Survey and the ZEW business expectations, I estimate static models and error-correction models explaining consumer expectations as functions of expert expectations, consumer retrospections, and lagged consumer expectations. I nd that consumer expectations are only weakly related to expert expectations, but strongly in uenced by consumers retrospections. Consumers primarily extrapolate perceptions about current economic conditions into the future. However, during national election campaigns, the relation between consumer and expert expectations is stronger. Keywords: rational expectations, consumer con dence, rationality JEL classi cation: E0, E32, D84 1 Introduction The rational expectation hypothesis (REH) is still standard in macroeconomic theory although recent empirical work sheds doubt on the validity of the REH. Probably Wirtschafts- und Sozialwissenschaftliche Fakultät, Universität Dortmund, D Dortmund, M.Roos@wiso.uni-dortmund.de 1

2 the best researched type of expectations are in ation expectations. Much evidence contradicts the REH for in ation expectations of private households 1. But also for other variables such as real family income, income growth, and general business conditions there exists evidence 2 that the expectations of private households are not rational in the strong sense of the REH 3. The REH is sometimes defended with the metaphor of the weather forecast 4. Many people are able to answer the question about tomorrow s weather correctly although they do not have particular meteorological knowledge and are not particularly a ected by the weather. In the aggregate, the lay weather forecast will probably be roughly identical to the professional forecast, because many laymen receive the same information about expert forecasts spread by the mass media. If people have some interest in future weather conditions, they should at least not ignore the expert information if it is available anyway. They should incorporate this information into their own forecasting procedure, which might consist in simple extrapolation from currently observed conditions or some other simple method accessible to laymen. This paper analyzes the relationship between expert expectations and consumer expectations about general economic conditions in Germany. In particular, I ask how strongly expert expectations about macroeconomic conditions enter into aggregate consumer expectations. If consumers believe that experts make forecasts superior to their own ones and have access to these expert forecasts, they should incorporate them into their forecasts. However, it might be that consumers do not only rely on expert expectations but on other variables as well when forming their macroeconomic 1 See Branch (2004) and the studies cited therein. 2 See Souleles (2004) and the studies cited therein. 3 The REH requires the expected value of a variable to be equal to the mathematical expectation in the relevant theoretical model. When I use the term "rational expectations" or "rational expectations in the strong sense" or "rational expectations in the sense of the REH" in the following, I always mean the mathematical expectation. 4 See Erikson et al. (2000). 2

3 expectations. I will analyze two candidate variables that might be important for the formation of aggregate consumer expectations: lagged consumer expectations and consumer retrospections, i.e. perceptions of current economic conditions relative to the past. If consumers form some kind of extrapolative or adaptive expectations, they are the main determinants of expectations. In the weather metaphor, this would mean that people look through the window and either update yesterday s forecast by the current weather conditions or simply forecast tomorrow s weather by today s using some folk wisdom. Following the political science literature, I also study whether elections have an in uence on consumers (or voters ) expectations about the economic future of the country. Forward-looking voters have an incentive to forecast the e ects of the candidates proposed economic policies. Before elections they hence may be more interested in reading the news and learning experts views than during other times. I test the hypothesis that experts in uence on consumers expectations is stronger during election campaigns. The paper s main contribution to the literature is twofold. First, I study consumers expectations about general economic conditions rather than in ation expectations which have been in the focus of the economic literature so far. Economists have analyzed consumer con dence or consumer expectations about general macroeconomic conditions mainly with respect to its predictive power for consumer spending or other macroeconomic variables 5. Although the studies of this kind are sometimes interpreted as tests on the rationality of consumer forecasts 6, they are rather tests on the relevance of the rational expectations permanent income hypothesis. How consumers expectations about general economic conditions are related to 5 See Ludvigson (2004) for a survey. 6 E.g. Acemoglu and Scott (1994). 3

4 the expectations of experts has not been studied in the economics literature so far, which is unfortunate for two reasons at least. Consumers ability and willingness to produce good forecasts about general economic conditions is important for political economic models which generally assume rational foreward-looking voters predicting their future utility 7. The political and economic predictions of these models clearly depend on the assumptions about consumers expectations. Furthermore, the analysis of consumers expectations about general economic conditions might yield insights in the process of expectation formation in general and the way how consumers use economic information. The second contribution of the paper is that I also analyze the dynamic relation between consumer and expert expectations. It might be that consumers expectations are strongly in uenced by expert expectations, but potentially with a time lag due to necessary di usion and learning time. Not everybody is a consumer of daily media news coverage. Some people learn news through personal conversation and discussions among friends, colleagues, and neighbors. This information channel might take up more time and might be prone to more error than the direct channel via newspapers or TV news. Rather than comparing consumer and expert expectations at each point in time, it seems more interesting to look at the long-term relationship between the two variables allowing for short-run dynamics as well. The appropriate model thus is an error-correction model, which I will estimate in addition to simple static models. To my knowledge, error-correction models have not yet been estimated in the context of consumer expectations. The main results are that aggregate consumer expectations are related to expert expectations but only weakly. Before national elections this relation is stronger than during the legislative period. Consumer expectations are much more in uenced by 7 See for example Rogo and Sibert (1988), Rogo (1990), Tabellini and Alesina (1990). 4

5 consumers evaluation of the current economic situation than by the expectations of experts. In the long-run relationships obtained from error-correction models, the importance of expert expectations is larger than in simple static models, but even in the long-run consumers rely much more on their own current evaluations than on expert expectations. Altogether, consumers primarily seem to extrapolate their perceptions about current economic conditions into the future. The paper is structured as follows: in Section 2, I present some theoretical considerations and brie y survey the literature which is closest to my analysis. Section 3 discusses the method of analysis and describes the data. Section 4 presents the results of a static analysis, and Section 5 contains the estimations for error-correction models. Section 6 concludes. 2 Theoretical considerations and related literature The REH is extremely demanding not only with respect to the economic expertise of agents, but also with respect to the assumptions about bene ts and costs from forming expectations. As already Feige and Pearce (1976) argued, rational agents should equate marginal bene ts and marginal costs from acquiring and processing information in the process of forming expectations. Rational expectations in the sense of the REH are only likely if marginal bene ts are positive and the information costs are zero. Economically rational expectations might imply that not all information is used and that the expectation is ine cient or even biased if the costs of forming expectations are nonnegligible and the bene ts are low. One can argue that for private households the bene ts of good expectations about macroeconomic variables are low and the costs are high. Households need good expectations about their economic futures rather than about the future of the national economy. Costs of gathering economic information and of transforming it into fore- 5

6 casts could be high since it requires considerable knowledge about the economy. However, it is not necessary that every individual forms rational expectations himself. One standard argument supporting the rational expectations hypothesis is that it is enough if some experts make rational forecasts and provide this information to the public. Consumers can incorporate this information into their own expectations leading to expectations of the representative consumer which are similar (or identical) to the expectation of the representative expert. Given that information about forecasts of macroeconomic variables by all sorts of professional economists abounds in the daily news, it should be easy for consumers to form macroeconomic expectations just adopting the forecasts read in the newspaper. Of course, the costs of this expectation formation procedure are not zero due to the huge amount of information which is spread by the mass media. Consumers have to incur costs sorting out relevant from irrelevant information and remembering the information when necessary. But for interested consumers these costs do not appear prohibitive. Concerning the personal bene ts of consumer expectations, there is a lot of evidence that people are not only interested in their own economic well-being but also in that of their nation. In the terminology of political scientists people are not wholly egocentric, but also sociotropic. Since the seminal work of Kinder and Kiewiet (1979, 1981) it has been empirically established and is now widely accepted in political science that at least with respect to economic voting, people also care about macroeconomic conditions in addition to their own pocketbook 8. Nadeau and Lewis-Beck (2001) demonstrate that consumers expectations about general economic conditions are a strong predictor of their vote in U.S. presidential elections, if both candidates run for election for the rst time, i.e. the current incumbent does not run for re- 8 See Lockerbie (2000) and the studies cited therein, also Gomez/Wilson (2001), Nadeau and Lewis-Beck (2001) and the studies cited therein, Caplan (2002), Drazen (2003, pp ). 6

7 election. Even if the personal bene t of correct macroeconomic expectations may be low, people seem to have some interest in the evolution of the economy. Together with the low cost of acquiring expert expectations this interest might be enough for a signi cant fraction of the population to have macroeconomic expectations similar to experts. Carroll (2003) theoretically shows how expert expectations transmitted by the media might enter into the aggregate expectations of consumers. He proposes a model of "sticky expectations" in which the households in ation expectations for the next year can be derived to be a convex combination of a current rational (or newspaper) forecast and last period s in ation expectation of households. The model assumes that households believe that actual in ation is the sum of an underlying fundamental in ation rate, which follows a random walk process, and an unforecastable transitory shock. Households furthermore believe that in ation forecasts of experts published in newspapers are more accurate than their own in ation estimates. Consequently, they use expert forecasts to update their own previous expectations in order to arrive at an estimate of the future fundamental in ation rate. Carroll estimates this model using in ation expectations by households from the Michigan Survey and by experts from the Survey of Professional Forecasters. He nds that in a regression where the coe cients of both expectation variables are constrained to sum up to one, the experts expectation weight is 0.27 meaning that in each quarter, about one-fourth of households have an in ation forecast identical to the (rational) expert forecast whereas three quarters have outdated expectations. Carroll also estimates his model for unemployment expectations and obtains essentially identical results. In the context of economic voting, Nadeau et al (1999) and (2000) analyze the relationships between expert expectations, consumer expectations, economic news, and objective economic indicators. In a three-step procedure, they rst analyze 7

8 how economic news are in uenced by objective economic indicators and by expert expectations and retrospections. Then, they regress consumer expectations on unemployment and in ation, expert expectations and retrospections, and on economic news. Finally, they regress presidential approval on the same variables. Nadeau et al. (1999) nd for the USA that economic news measured by the balance of favorable and unfavorable economic newspaper stories depend on economic indicators and expert retrospections, but not on expert expectations. Consumer expectations, in turn, are strongly in uenced by expert retrospections and economic news in addition to past consumer expectations. Interestingly, expert expectations are not statistically signi cant. Nadeau et al. (2000) present the same analysis for Great Britain 9. Here they nd that economic news are related to expert expectations in addition to economic indicators. Again, consumers macroeconomic expectations depend on past expectations and economic news but not on expert expectations. They conclude in both analyses that there is no straightforward transmission of expert expectations via the mass media into consumer expectations. For the sake of clarity I summarize these theoretical considerations in four hypotheses which are the basis of my empirical analysis: Hypothesis 1 Consumer expectations are positively related to expert expectations. Although the private bene ts of acquiring expert information in order to improve one s expectations about general economic conditions may be low, I claim that the costs are even lower. It is almost impossible not to get in touch with economic forecasts of experts if one consumes news. Since it does not require to exert additional e ort to learn expert expectations for any news consumer and many people are regular news consumers 10, consumer expectations should be correlated with expert 9 For Great Britain, however, expert retrospections are not available. 10 The ve most important TV news broadcasts alone already reach about one quarter of Ger- 8

9 expectations. Hypothesis 2 The correlation between expert and consumer expectations is low. The costs of learning expert expectations may be close to zero for many consumers, but the costs of processing and remembering this information is positive. Since in general consumers to not bene t much from improving their macroeconomic forecasts, they will often not remember expert forecasts. Hypothesis 3 During national election campaigns the in uence of expert expectations on consumer expectations is higher than otherwise. Prospective and sociotropic economic voters compare the expected economic performance of all candidates or parties. Before elections the bene ts of good macroeconomic forecasts are higher than after elections. Therefore consumers pay more attention to expert expectations and exert more e ort in the processing of this information. Hypothesis 4 Consumer expectations are positively related to past consumer expectations and consumer retrospections. Other information which is readily available to consumers when forming expectations are their own past expectations and assessments of the current state of the economy. Since unexpected large changes of general economic conditions are rare - in other words there is strong autocorrelation in GDP - simple autoregressive forecasting rules are reasonable. 3 Method and data Method As in Carroll (2003), I will estimate a simple static model in which consumer expectations, ce; are a linear combination of past expectations and expert many s total population (see 9

10 expectations, zew; ce t = ce t zew t + " t : (1) I will estimate an unconstrained version and a version with the constraint = 1: The constrained version provides a measure of the degree of rationality of the consumer expectations. Expert expectations are more important for consumer expectations making them more rational the closer 2 is to one. For a comparison, I also include consumer retrospections, cr;, i.e. how consumers assess the current situation relative to the past 12 months, as a regressor: ce t = ce t cr t + " t (2) ce t = ce t zew t + 3 cr t + " t : (3) In order to test Hypothesis 3, I include an interaction term campzew in equations (1) and (3), where camp is a dummy variable equal to one within the six months before national elections 11. I chose the campaign length to be six months, because this is the forecast horizon of the experts. The approach in Carroll (2003) and in Nadeau et al. (1999, 2000) is to t a static relationship between consumer and expert expectations. As argued in the Introduction, there might exist short-run deviations of consumer expectations from expert expectations, but also a stable long-run relationship. If this is the case, the static models would underestimate the true importance of expert expectations. In 11 During the sample period, national elections took place on October 16, 1994, September 27, 1998, and September 22,

11 order to study this issue, I estimate error-correction models: ce t = ce t zew t zew t + " t (4) ce t = ce t cr t cr t + " t (5) ce t = ce t zew t zew t + 4 cr t cr t + " t : (6) From the error-correction models, one can infer the long-run relationships ce = zew; ce = cr; and ce = zew 4 1 cr: They can be compared with the corresponding results from the static models. In addition, 1 measures the speed of adjustment to the long-run relationship allowing to calculate the half-life of the gap between actual expectations and the long-run relationship. Data The data is taken from two publicly accessible sources. I measure aggregate consumer expectations and retrospections by the corresponding series in the European Commission s consumer con dence indicator for Germany. Question 3 in the consumer survey questionnaire (see European Commission 2004) asks for retrospective evaluations of the past 12 months and question 4 for future expectations over the next 12 months 12. The resulting time series cr for the retrospections and ce for the expectations are the respective balances of shares of positive and negative answers. Accordingly, the maximum value is 100, if everybody has optimistic expectations or retrospections, and the minimum is -100 if everybody is pessimistic. Expert expectations are measured by the ZEW 13 Indicator of Economic Sentiment 12 The exact wording of the questions is: Q3 How do you think the general economic situation in the country has changed over the past 12 months? It has 1 got a lot better, 2 got a little better, 3 stayed the same, 4 got a little worse, 5 got a lot worse, 9 don t know. Q4 How do you expect the general economic situation in this country to develop over the next 12 months? It will 1 get a lot better, 2 get a little better, 3 stay the same, 4 get a little worse, 5 get a lot worse, 9 don t know. 13 ZEW: Centre for European Economic Research, Mannheim, Germany 11

12 (ZEW-Konjunkturerwartungen). This indicator is constructed in the same way as the consumer indices. However, the respondents are about 350 professional economists mainly from banks (77%), insurance companies (14%), and some large industrial rms (9%). All of them do business cycle analysis and forecasting for their employers and customers. The ZEW indicator time series is labelled zew: Unfortunately, the time horizon for the experts expectations is 6 instead of 12 months. Nevertheless, expert expectations should be informative for consumer expectations even for di erent time horizons, because consumers are asked for an assessment of the likely development over the next 12 months and not for a point forecast for a state 12 months later 14. In order to check whether this di erence in forecast horizons is important, I regress zew t on zew t 3 and zew t 4 : The prediction, zew t;t+12 ; from this regression can be interpreted as a twelve months forecast based on the information available at the time of the forecast. All time series are available as seasonally adjusted monthly series from December 1991 to June 2004 making 151 observations. The three series are highly autocorrelated. However, after testing for unit roots, I conclude that all of them are level stationary 15. Stationarity is reasonable, because by construction the indices are bounded from below and above. Descriptive analysis Table 1 summarizes the data. Comparing means, consumers aggregate expectations are signi cantly (p<.001) more optimistic than their retrospections. Experts expectations on average are even more optimistic, but they also uctuate more. 14 Di erent forecast horizons are also present in Souleles (2004) and Nadeau et al. (1999, 2000). Nadeau et al. (1999) argue that the time frame of the questions to consumers does not really matter empirically. The same conclusion is drawn in Conover et al. (1986) who nd that survey respondents seem to ignore the time frame of questions. Finally, evidence from the Michigan Survey supports this claim: the correlation between consumer expectations over the next twelve months and the next ve years in the period from May 1959 until December 2003 is very high (.903). 15 See the Appendix for results. 12

13 N Mean Std CV Min Max ce cr zew Table 1: Descriptive statistics Visual inspection of the series (Figure 1) shows that there is only a low contemporaneous correlation between ce and zew (r = :192; R 2 = :037). Often, expert and consumer expectations are almost exactly opposite, i.e. the majority of consumers is optimistic while experts are pessimistic and vice versa. This is especially obvious during the period between January 1996 and January From Figure 1, one might get the impression that expert expectations lead consumer expectation, which might be reasonable if consumers learn and incorporate expert expectations with a time lag. The cross-correlogram in Figure 2 shows that, indeed, the correlation between the series is maximal (r = :58; R 2 = :336) if zew is lagged by 9 months. This is somewhat surprising because of the di erent time horizons of the expectations. If both time series correctly anticipated the future state of the economy at 6 and 12 months respectively, ce should lead zew by 6 months. If zew is a reliable indicator at a 6-months-horizon, ce actually lags the reference cycle. In fact, zew was found to lead industrial production by 7 to 9 months (Hinze 2003). From Figure 1, it is clear that the di erence of ce and zew will not be a random time series. Indeed, the Box-Pierce test clearly rejects the null of white noise (p < :0001; 10 lags) for the di erence in expectations due to high autocorrelation. This means that consumer expectations di er systematically from expert expectations. Comparing consumer expectations with retrospections, it is again instructive to start the analysis with a visual inspection of the two time series (Figure 3). Now, we see a striking covariation. Without any formal testing, we recognize a high contemporaneous correlation between ce and cr: 13

14 Figure 1: Consumer expectations and expert expectations from 1991:12 to 2004:6 Figure 2: Cross-correlations between consumer and expert expectations 14

15 Figure 3: Consumer expectations and retrospections from 1991:12 to 2004:6 The cross-correlogram in Figure 4 supports this impression: At lag 0 the correlation coe cient is.889 (R 2 = :79) and the highest correlation of.896 (R 2 = :8) is at lag -1 meaning that ce leads cr by one month. Given this evidence, we might preliminarily conclude that consumer expectations are only weakly in uenced by current expert expectations. It rather seems that consumers form their expectations primarily on the basis of their assessment of the current economic situation. Forecast target of households and experts Although the wordings of the expert survey and the consumer survey are very similar and both groups are asked for the expectation about general economic conditions, it might be that experts and consumers have di erent indicator variables in mind when answering the question. For instance, experts might think of leading business cycle indicators such as stock 15

16 Figure 4: Cross-correlations of consumer expectations and retrospections market indexes or business pro ts, whereas consumers think of other indexes such as unemployment or the real wage. Despite this theoretical possibility, I argue that both experts and consumers forecast target is likely to be GDP as a broad and coincident indicator of the state of economic activity. One argument is that both groups are also asked to provide expectations for other variables. The ZEW asks the experts to forecast qualitatively the evolution of the rate of in ation, interest rates, the stock market, exchange rates, and the oil price in addition to general economic conditions. Several studies have found the ZEW index of general economic conditions to be a leading indicator of industrial production and GDP 16. The EU consumer survey contains questions about consumers expectations of their own nancial future, in ation, and unemployment. Consumers expectations of general economic conditions is strongly correlated to 16 See Hüfner/Schröder (2001, 2002), Hinze (2003). 16

17 (1) (2) (3) (4) ip t ip t ip t ip t const (.082) (.108) (.151) (.181) ip t (.079) (.077) (.080) (.079) ip t (.079) (.077) (.080) (.080) zew t (.002) (.002) ce t (.007) (.007) R AIC p(q) N Table 2: Industrial production as forecast target of households and experts; AIC: Akaike information criterion; p(q): empirical signi cance level of Box-Pierce test on autocorrelated residuals at 15 lags; standard errors in parentheses; * signi cant at 10 percent, ** significant at 5 percent, *** signi cant at 1 percent the income expectations (.87) and unemployment (-.89), and weakly correlated to in ation (-.33). Since private income and unemployment are coincident indicators to GNP in Germany 17, consumers also forecast GDP when asked about general economic conditions even if they think of unemployment. In addition to this indirect evidence, Table 2 presents direct evidence that consumer and expert expectations target GDP or production. I regressed monthly industrial production 18 on two lags and lagged expert and consumer expectations. Both expert and consumer expectations predict industrial production. When both variables are together included in the regression, consumer expectations are not signi cantly di erent from zero, whereas expert expectations are. Altogether experts and consumers to not think of totally di erent variables when asked about their expectations of future general economic conditions. For both groups, GDP or industrial production is a measure of general economic conditions. 17 See e.g. Maußner (1994). In the U.S. unemployment is generally seen as a lagging indicator. 18 Monthly data on GDP is not available, but industrial production is often used as a proxy. The series is ltered with the HP lter. 17

18 4 Static analysis Results Table 3 contains the OLS-estimation results of the static model (1). As a benchmark, I rst present a simple AR(1) model in column (1). This model already ts quite well and con rms the high autocorrelation of ce found in the previous section. Column (2) presents the results of an unconstrained estimation of (1). The coe cient of zew is signi cantly positive but small, supporting both Hypothesis 1 and Hypothesis 2. Notice that the inclusion of zew increases the adjusted R 2 only slightly. The model with zew t;t+12 ; i.e. the predicted 12-months forecasts of experts, in column (3) yields the same results as the model with the original series in column (2). This con rms my previous claim that the di erent time horizons do not drive the results. In (2), the sum of 1 and 2 is close to one, which cannot be rejected statistically (p=.42). Not surprisingly, the constrained estimation in column (4) yields very similar results. 2 = :034 indicates the percentage of consumers who change their expectation relative to the percentage of experts. Even if all experts turned from pessimistic expectations to optimistic expectations, leading zew to rise from -100 to 100, only 3.4 percent of consumers would change their expectations in the same way. In order to get another sense of magnitudes, I estimated the constrained model with the variables normalized to have zero mean and a standard deviation of one. The results are listed in column (5): a one-standard deviation change of ce t 1 changes ce by 92.4% of a standard deviation, and a change of zew by one standard deviation changes ce by 7.6% of a standard deviation. Very interesting is the result in column (6) where I included an election campaign dummy interacting with zew. The coe cient of this variable is strongly signi cant and large relative to the zew coe cient. During the six months before a national 18

19 (1) (2) (3) (4) (5) (6) ce t ce t ce t ce t ce t ce t raw raw raw raw normalized raw zew t;t = = 1 const (.48) (.53) (.64) (.45) (.52) ce t (.02) (.02) (.02) (.007) (.016) (.021) zew t (.007) (.009) (.007) (.016) (.007) camp zew t.034 (.012) AIC R p(q) p(d) N Table 3: Static models, raw: original data, normalized: variables with mean zero and std. dev one, AIC: Akaike information criterion, p(q): empirical signi cance level of Box-Pierce test on autocorrelated residuals at 15 lags, p(d): empirical signi cance level of Durbin s alternative test for autocorrelation, standard errors in parentheses, * signi cant at 10 percent, ** signi cant at 5 percent, *** signi cant at 1 percent election, consumers pay more attention than usual to expert expectations. In preelection times, their expectations are more strongly in uenced by experts forecasts. The data thus also support Hypothesis 3. A Ramsey RESET test after the unconstrained estimation of (1) rejects the null of no omitted variables at the 5% level. This can be interpreted as rst evidence that consumers do not restrict their attention to ce and zew when forming expectations. Alternatively, consumers might form extrapolative expectations based on their current evaluation of economic conditions. Column (1) of Table 4 contains the estimated coe cients of model (2). Notice that the Durbin test and the Q-test indicate autocorrelation in the residuals. This problem is also present in column (2), where zew is included in the model. As column (3) shows, the autocorrelation can be removed by controlling for the autocorrelation of cr. Introducing lagged retrospections improves the model t signi cantly and yields uncorrelated residuals. Although the RESET test does not reject the null of no omitted variables in (3) at 10%, I extend 19

20 (1) (2) (3) (4) (5) ce t ce t ce t ce t ce t const (.52) (.55) (.32) (.36) (.36) ce t (.05) (.05) (.03) (.03) (.03) cr t (.03) (.03) (.05) (.05) (.05) cr t (.05) (.05) (.05) zew t (.007) (.005) (.004) camp zew t.015 (.008) AIC R p(q) p(d) N Table 4: Extended static models, raw: original data, normalized: variables with mean zero and std. dev one, AIC: Akaike information criterion, p(q): empirical signi cance level of Box- Pierce test on autocorrelated residuals at 15 lags, p(d): empirical signi cance level of Durbin s alternative test for autocorrelation; standard errors in parentheses, * signi cant 10 percent, ** signi cant at 5 percent, *** signi cant at 1 percent the model by zew in column (4). Expert expectations are statistically signi cant at the 5% level, but they improve the model only minimally: the AIC drops slightly, but the adjusted R 2 almost does not change. Since the null hypotheses that the coef- cients in (4) sum up to one is clearly rejected (p < :001); I do not perform restricted estimations. Since cr t and cr t 1 are signi cant regressors, Hypothesis 4 cannot be rejected. When the campaign variable is included in the regression, the zew coe cient is slightly less precisely estimated (p = 0:052): The campaign coe cient is positive and larger than the zew coe cient, but only weakly signi cant (p = :076): Nevertheless the previous nding is con rmed that during election campaigns expert expectations have a stronger in uence on consumer expectations than in non-election periods. Discussion None of the four hypotheses in Section 2 can be rejected. From both, the simple static model (1) and the extended model (3), I conclude that consumers 20

21 only make minimal use of publicly available expert expectations when forming theirs. On average, at most 3.5% of all consumers would change their expectations if all experts changed their expectations in the same direction from one to the other extreme. The most important input for consumers are their own past expectations. However, during national election campaigns, consumers pay more attention to expert expectations. The in uence of experts on consumer expectations more than doubles during the six months before national elections. Since consumers only marginally rely on experts, their expectations appear not to be rational in the strong sense. They ignore valuable information provided by economic experts which is publicly available and of better quality than the expectations of consumers (see Table 2). 5 Dynamic analysis The static analysis showed that the most important input to the expectation formation of consumers are their own past expectations, whereas current retrospections or expert expectations, which is new information, have only small weights. While this is interesting in itself, it may underestimate the importance of expert expectations. Expert expectations might be an important long-run determinant of consumer expectations, which could be hidden by short-run deviations. Consumers might rationally ignore expert expectations most of the time, but consider this information if the gap between their own expectations and those of experts becomes too large. If this is the case, the error-corrections models (4) to (6) are more appropriate than simple static models. As benchmarks, I rst regress the change of consumer expectations only on the change of expert expectations and consumer retrospections respectively. The results are presented in column (1) and (2) of Table 5. ce is unrelated to zew; but 21

22 strongly correlated with cr: Column (3) contains the estimation result of model (4) without consumer retrospections. The overall t of the model is rather low and the coe cient of the short-run dynamics 3 is still insigni cant. The longrun relation between ce and zew is 0.6, meaning that a 10 point increase in the balance of optimistic versus pessimistic experts leads to a 6 point increase in consumer expectations, which is a much larger e ect than those found in the static models. The speed of adjustment to the long-run equilibrium (0.05) is low implying a half-life of almost 14 months. In sum, there seems to be a positive long-run relationship between consumer expectations and expert expectations. However, consumer expectations lag expert expectations considerably. In the short-run, consumer expectations are una ected by changes in expert expectations. For a comparison, I present the estimated error-correction model (5) with consumer retrospections alone in column (4). Notice that this model is equivalent to the model in column (3) of Table 2. This model ts the data much better than the ECM with expert expectations. The long-run relation between ce and cr is.45 with a speed of adjustment of -.11 implying a half-life of 6.3 months. Combining the two models yields (6) whose estimated coe cients are shown in column (5). The parameter estimates are similar as before. Now, the short-run e ect of expert expectations is weakly signi cant at the 9% level. We nd a long-run relation between all three variables 19 : ce = 6:57 + : 09 zew + :44 cr (3:56) (:04) (:09) As in the static models, the long-run e ect of zew on consumer expectations is much lower once I control for cr. Even in the long-run, consumer retrospections are much more important for consumer expectations than expert expectations. I 19 Standard errors in parentheses. 22

23 (1) (2) (3) (4) (5) ce t ce t ce t ce t ce t const (.29) (.18) (.55) (.32) (.37) ce t (.02) (.03) (.03) zew t (.01) (.005) zew t (.03) (.03) (.02) cr t (.02) (.02) cr t (.05) (.05) (.05) AIC R p(q) DW N Table 5: Error correction models, AIC: Akaike information criterion, p(q): empirical signi cance level of Box-Pierce test on autocorrelated residuals at 15 lags, DW: Durbin-Watson statistic, standard errors in parentheses, * signi cant 10 percent, ** signi cant at 5 percent, *** signi cant at 1 percent tested whether the long-run coe cients can be restricted to sum to one, which was rejected at the 1% level. Together with the signi cant intercept this indicates that some other factors in addition to expert expectations and consumers evaluations of current conditions determine expectations. From this analysis, I conclude that aggregate consumer expectations are positively related to consumer retrospections and to expert expectations. However, both in the short run and in the long run consumer retrospections are much more important for consumer expectations than expert expectations. In the long run, a one-standard deviation change of zew results in a.28-standard deviation change of ce; whereas a change in cr by one standard deviation changes ce by 67% of a standard deviation. Notice that controlling for short-run dynamics indeed does matter. Although still low, the importance of expert expectations in the long run relation is higher than in the simple static models. 23

24 6 Conclusions Expectations are important in many elds of macroeconomics. In business cycle theory, they are sometimes seen as causes of economic uctuations, e.g. in Pigou (1927) and Keynes (1936) or, more recently, in sunspot models such as Chauvet and Guo (2003). In all macroeconomic models, in which economic voting plays a role 20, the expectations of voters are decisive for the political and the economic equilibrium. Much in line with previous empirical research, I nd that consumers do not form their expectations rationally in the strong sense of the rational expectations hypothesis. To take up the weather metaphor, it seems that consumers surprisingly do not listen carefully to experts weather forecasts on TV or radio, but rather look through the window when predicting whether the next day will be sunny or rainy. They largely ignore short-run changes of available forecasts by experts and even in the long run do not fully incorporate this information into their expectation formation. Consumers aggregate expectations are largely extrapolative, since they are strongly correlated with consumers assessments of current economic conditions. The result that consumers ignore cheap but relevant information can be interpreted in two ways. One can argue that consumers form their macroeconomic expectation in a way which is economically rational. Most of the time, when the quality of the forecasts does not really matter, they largely use adaptive expectations, which are simple and inexpensive. However, in times when macroeconomic conditions are important for their behavior, i.e. before elections, they increase their e ort to incorporate expert expectations into their forecasting procedure. Consumers thus only incur costs to improve their expectations, when marginal bene ts are higher than marginal costs. 20 See the New Political Economy literature summarized in Drazen (2003). 24

25 However, although I nd that the importance of expert expectations increases before elections, the total weight of expert expectations in consumer expectations is still very low. An interpretation alternative to the one that consumers are economically rational and the bene ts of expectations are low is that consumers misperceive the value of expert expectations. Either consumers think they do not need expert information, as they are able to form good expectations themselves, or are not aware that incorporating expert assessments into their own might improve expectations. It is also possible that laymen reach other conclusions about future economic conditions than experts and trust their own results more than those of experts. Either way, some kind of overcon dence 21 might play a role in the formation of consumer expectations and induce consumers to ignore valuable information. How agents actually form their economic expectations is largely unknown. For thirty years, the Rational Expectations Hypothesis has exempted one part and hindered the other part of economists from studying the actual formation of expectations. Since the empirical evidence against the REH is accumulating, we should start to do basic research on the formation of expectations using a broad variety of methods ranging from experiments via survey methods to econometric analysis of available expectation data 22. In particular, there seems to be potential for a fruitful exchange of ideas with economic psychologists 23 and to follow the route of behavioral (macro-)economics proposed by Katona (1968) and recently resuggested by Akerlof (2002). 21 Kruger and Dunning (1999) show that many people overestimate their abilities in various domains. They argue, however, that a mimimal threshold of theory, knowledge, or experience must be reached for people to overestimate themselves. If people do not have even an intuition how to perform a certain task, they do not tend to be overcon dent. It seems likely that practically everybody has some (naïve) understanding of economic issues, because everybody is involved in numerous economic issues in daily life. 22 See Manski (2004) for a similar call for basic research on expectation formation. 23 See Rabin (1998). 25

26 Appendix Stationarity Table 6 reports the results from GLS-Dickey-Fuller tests (null hypothesis: unit root) and KPSS tests (null: level stationarity): ce cr zew GLS-DF test stat lag KPSS test stat lag Table 6: Tests for level stationarity; lag: optimal lag according to Ng-Perron sequential t-testing, ***: signi cant at 1 percent For ce and cr, both test deliver the same result. The GLS-Dickey-Fuller test rejects the null of a unit root strongly and the KPSS test does not reject the null of level stationarity. Both series can hence safely treated as level stationary. For zew; the tests disagree. The GLS-Dickey-Fuller does not reject the unit root hypothesis, but the KPSS does not reject stationarity. In order to break the tie, I also performed ADF tests and the Phillips-Perron tests. All ADF tests with the number of lags between 1 and 14 clearly reject the unit root at least at the 5% level and often even at 1%. The Phillips-Perron test yields similar results: with the exception of one lag, all tests with the number of lags between 2 and 14 reject the unit root at least at 5%. With only one lag, the unit root is rejected at the 5.56% level. Given this evidence, I also consider zew as level stationary. References Acemoglu, D. and A. Scott (1994), Consumer con dence and rational expectations - Are agents beliefs consistent with the theory, Economic Journal, vol. 104, pp Akerlof, G. A (2002), Behavioral macroeconomics and macroeconomic behavior, American Economic Review, vol. 92, pp

27 Branch, W.A. (2004), The theory of rationally heterogeneous expectations: Evidence from survey data on in ation expectations, Economic Journal, vol. 114, pp Caplan, B. (2002), Sociotropes, systematic bias, and political failure: Re ections on the survey of Americans and economists on the economy, Social Science Quarterly, vol. 83, pp Carroll, C. D. (2003), Macroeconomic expectations of households and professional forecasters, Quarterly Journal of Economics, vol. 118(1), pp Conover, P. J., S. Feldman, and K. Knight (1986), Judging in ation and unemployment: The origins of retrospective evaluations, Journal of Politics, vol 48, pp Chauvet, M. and J. T. Guo (2003), Sunspots, animal spirits, and economic uctuations, Macroeconomic Dynamics, vol. 7, pp Drazen, A. (2003), Political Economy in Macroeconomics, Princeton. Erikson, R. S., M. B. MacKuen, and J. A. Stimson (2000), Bankers or peasants revisited: economic expectations and presidential approval, Electoral Studies, vol. 19, pp European Commission (2004), The joint harmonised EU programme of business and consumer surveys user guide (updated 26/5/2005) Feige, E.L. and D.K. Pearce (1976), Economically rational expectations: Are innovations in the rate of in ation independent of innovations in measures of monetary and scal policy?, Journal of Political Economy, vol.84, pp

28 Gomez, B.T. and J.M. Wilson (2001), Political sophistication and economic voting in the American electorate: A theory of heterogeneous attribution, American Journal of Political Science, vol. 45, pp Hinze, J. (2003), Prognoseleistung von Frühindikatoren, HWWA Discussion Paper 236. Hüfner, F. P. and M. Schröder (2001), Unternehmens- versus Analystenbefragungen - Zum Prognosegehalt von ifo-geschäftserwartungen und ZEW-Konjunkturerwartungen, ZEW-Discussion Paper Hüfner, F. P. and M. Schröder (2002), Prognosegehalt von ifo-geschäftserwartungen und ZEW-Konjunkturerwartungen: Ein ökonometrischer Vergleich, Jahrbücher für Nationalökonomie und Statistik, vol. 222, pp Katona, G. (1968), Behavioral and ecological economics - Consumer behavior: Theory and ndings on expectations and aspirations, American Economic Review, vol. 58, pp Keynes, J.M. (1936), The General Theory of Employment, Interest, and Money, New York Kinder, D.R. and D. R. Kiewiet (1979), Economic discontent and political behavior: The role of personal grievances and collective economic judgments in congressional voting, American Journal of Political Science, vol. 23, pp Kinder, D. R. and D.R. Kiewiet (1981), Sociotropic politics: The America case, British Journal of Politics vol. 11, pp Kruger, J. and D. Dunning (1999), Unskilled and unaware of it: How di culties in recognizing one s own incompetence lead to in ated self-assessments, Journal of Personality and Social Psychology, vol. 77, pp

29 Lockerbie, B. (2000), Economics and politics: Egocentric or sociotropic, Paper prepared for delivery at the 2000 Annual Meeting of the Midwest Political Science Association, April 27-30, Chicago. Ludvigson, C. S. (2004), Consumer con dence and consumer spending, Journal of Economic Perspectives, vol. 18(2): Manski, C. F. (2004), Measuring Expectations, Econometrica, vol. 72, pp Maußner, A. (1994) Konjunkturtheorie, Springer, Berlin Nadeau, R. and M.S. Lewis-Beck (2001), National economic voting in U.S. presidential elections, Journal of Politics, vol. 63, pp Nadeau, R., R.G. Niemi, D.P. Fan, and T. Amato (1999), Elite economic forecasts, economic news, mass economic judgements, and presidential approval, Journal of Politics, vol. 61, pp Nadeau, R., R.G. Niemi, and T. Amato (2000), Elite economic forecasts, economic news, mass economic expectations, and voting intentions in Great Britain, European Journal of Political Research, vol. 38, pp Pigou, A.C. (1929) Industrial Fluctuations, London Rabin, M. (1998), Psychology and economics, Journal of Economic Literature, vol. XXXVI, pp Rogo, K. (1990), Equilibrium political budget cycles, American Economic Review, vol. 80, pp Rogo, K. and A. Sibert (1988), Elections and macroeconomic policy cycles, Review of Economic Studies, vol. LV, pp