why did forecasters fail to predict the 1990 recession?

International Journal of Forecasting 15 (1999) 309–323

Why did forecasters fail to predict the 1990 recession?

*David Fintzen, H.O. SteklerDepartment of Economics, George Washington University, Washington, DC 20052, USA

Abstract

This paper examines the forecasts that were prepared prior to and during the early stages of the recession that occurred in1990 in the United States. It examines the characteristics of those forecasts, the data that were available and attempts todetermine why the forecast errors occurred. Private sector and public sector predictions are compared and the possibility ofrational forecast bias is investigated. We conclude that data problems might have contributed to the forecast errors andsuggest that individuals might have been able to predict this recession. 1999 Elsevier Science B.V. All rights reserved.

Keywords: Recession; Forecast errors; Rational forecast bias; Macroeconomic forecasting

1. Introduction The implication of these evaluations is that fore-casters should have been able to predict the turning

One of the most disturbing findings of forecast points. Several studies have attempted to provide aevaluations is that, in the United States, recessions theoretical explanation for forecasters’ failure tohave generally not been predicted prior to their predict these turning points (Fels and Hinshaw,occurrence. This is true for both annual and quarterly 1968; Stekler, 1972). Stekler (1972) showed that aestimates (McNees, 1991; Zarnowitz, 1991). For turning point would not be predicted if forecasters’example, both the 1974 and 1981 peaks were not prior probabilities about the likelihood of a recessionrecognized even as they occurred. While missing the were low. Schnader and Stekler (1998) extended thisactual turning points, the forecasts did indicate that analysis by demonstrating that asymmetric costs

1the economy would be slowing down . associated with predicting false turns versus notcalling a true one might also produce this result(Their model is presented in the Appendix A).

These theoretical explanations for turning point*Corresponding author. Tel.: 11-202-9946150; fax: 11-202- errors have as yet not been confirmed by empirical

9946147.studies that document the reasons why cyclicalE-mail address: [email protected] (H.O. Stekler)

1 downturns were not predicted in advance. Nor isThis ability to distinguish between slow or negative growth andrapid growth was also observed in the predictions of three there evidence that conclusively demonstrates that,econometric services (Schnader and Stekler, 1990; Stekler, 1994). given the information that was available in real time,Only with a lag did the forecasters recognize that the ‘slowdowns’ forecasters should have been able to predict thesehad turned into recessions. The Stekler (1994) analysis of the

turning points. Examining the actual data and thepredictions of three econometric forecasting services indicated thatforecasts that were available prior to a particularthis recognition sometimes occurred after the quarter in which the

economy had its peak (also see, Fels and Hinshaw, 1968). recession might yield insights about this problem.

0169-2070/99/$ – see front matter 1999 Elsevier Science B.V. All rights reserved.PI I : S0169-2070( 98 )00072-7

310 D. Fintzen, H.O. Stekler / International Journal of Forecasting 15 (1999) 309 –323

3This paper analyzes the forecasts made in 1990 to The mean GNP growth rate forecasts for 1990determine when the cyclical peak that occurred in and 1991 as well as the actual changes, as measuredJuly of that year was first predicted. We first examine by the data released one and two months after thea number of sets of forecasts issued between late end of each quarter, are presented chronologically in1989 and late 1990 and analyze specific statistical Table 1. The actual changes are included in thischaracteristics of these predictions. These charac- table, because these preliminary estimates of GNPteristics include the mean and/or median forecasts, are the numbers that most forecasters are trying tothe number of individuals who were predicting predict.negative growth, as well as the statistical distribution The mean of the predictions of every surveyof the projections to determine the associated uncer- conducted between late 1989 and early 1990 showedtainty. The next section looks at the data that became the economy growing slowly but not entering aavailable in the months just before and just after the recession. Moreover, in the surveys conducted be-actual cyclical peak. The purpose is to determine tween the end of 1989 and mid-1990, there were nowhether or not the data that were available in real substantial downward revisions in the forecasts fortime were clearly signalling a turn that was neither the second half of 1990 or the first half of 1991. Thepredicted in advance nor identified contempora- Wall Street Journal survey published in early Julyneously. Finally, we review previous studies that predicted a lower growth rate than did the othersought to determine the causes of the recession of surveys, but the mean forecast was still one of1990–1991. If the causes of this cycle were similar sluggish growth through the middle of 1991.to those of previous recessions, then the insights After the Iraqi invasion of Kuwait in early Augustabout these forecast failures might be applicable to and the concomitant spike in oil prices, the forecastscyclical predictions in general. were revised downward, and by September the

possibility of a short recession beginning in 1990.4was recognized. Although the official date marking

2. The forecasts for 1990 the beginning of the recession is July, it was in theOctober /November time-frame when the mean fore-

In the course of any year there are many surveys cast of those surveyed in Economic Forecasts orin which different individuals provide economic those participating in the Blue Chip sample showedforecasts for the next several quarters or for a two consecutive quarters of decline in real GNPcalendar year. These surveys appear in different (Two quarters of negative real growth is the general-

4publications. Our analysis of the 1990 forecasts is ly accepted definition of a recession) .based on surveys published in: (1) Business Week atthe end of 1989, (2) The Wall Street Journal inJanuary, July and August 1990, (3) Economic 3. Was there a ‘consensus’?

2Forecasts , monthly from December 1989 throughNovember 1990, and (4) the Blue Chip Indicator Our analysis to this point has focussed on theForecasts from December 1989 through November mean of the forecasts of every survey. The evidence1990. These data sources cannot be treated as indicates that the mean forecast did not change muchindependent samples because the predictions of aparticular forecaster may have been included in 3The median forecasts do not differ substantially from these meanseveral of the surveys that were conducted at approx- predictions.

4imately the same time. There are some who question whether this mechanical definitionof a recession is appropriate (McNees, 1991). Alan Greenspan,Chairman of the Board of Governors of the Federal Reserve,

2This is a monthly publication of Elsevier that surveys and expressed a similar view in late September 1990. He indicated thatpublishes the home country economic forecasts of a number of a recession was a decline that fed upon itself (Wall Street Journal,individuals located in different nations. Victor Zarnowitz collected 21 September 1990). However, even if forecasters accept thisand provided the interpretations of the US economic forecasts for view, the failure to predict even a one quarter decline in GNPthis period. would be prima facie evidence of a failure to predict a recession.

D. Fintzen, H.O. Stekler / International Journal of Forecasting 15 (1999) 309 –323 311

Table 1Forecast of the growth rate of GND (%)

Date of forecast Source (no. of people) Forecast of the growth rate of GND (%) made for:

90.1 90.2 90.3 90.4 91.1 91.2

Dec. 89 Blue Chip (51) 1.3 1.7 2.0 2.4 — —Dec. 89 Economic Forecasts (31) 1.4 2.2 2.4 2.5 — —Dec. 89 Business Week (50) 1.4 1.9 2.4 2.6 — —

a a b bJan. 90 Wall St. J. (40) 1.3 1.3 2.0 2.0 — —Jan. 90 Blue Chip (50) 1.3 1.7 2.0 2.2 — —Jan. 90 Economic Forecasts (29) 1.4 2.1 2.3 2.5 — —Feb. 90 Blue Chip (50) 1.1 1.7 2.2 2.4 2.4 —Feb. 90 Economic Forecasts (27) 1.3 1.9 2.3 2.5 2.5 —Mar. 90 Blue Chip (51) 1.2 1.9 2.3 2.3 2.3 —Apr. 90 Blue Chip (51) 1.6 1.9 2.2 2.2 2.3 —Apr. 90 Economic Forecasts (32) 1.8 2.0 2.2 2.2 2.3 —Apr. 90 Act. 1st est. 90.1 2.1 — — — — —May 90 Blue Chip (51) — 2.1 2.1 2.1 2.3 2.4May 90 Ecomonic Forecasts (29) — 2.5 2.1 2.1 2.3 2.5May 90 Act. 2nd est. 90.1 1.3 — — — — —June 90 Blue Chip (51) — 2.1 2.3 2.1 2.3 2.4June 90 Economic Forecasts (29) — 2.4 2.4 2.0 2.4 2.4

b b c cJuly 90 Wall St. J. (40) — — 1.6 1.6 1.8 1.8July 90 Blue Chip (52) — 1.8 2.1 1.9 2.2 2.3July 90 Economic Forecasts (29) — 1.9 2.2 1.9 2.3 2.4July 90 Act. 1st est. 90.2 — 1.2 — — — —

b b c cAug. 90 Wall St. J. (34) — — 0.3 0.3 1.0 1.0Aug. 90 Blue Chip (49) — — 1.4 0.9 1.6 2.0Aug. 90 Economic Forecasts (30) — — 1.0 20.7 1.5 2.0Aug. 90 Act. 2nd est. 90.2 — 1.2 — — — —Sep. 90 Blue Chip (52) — — 1.0 0.0 0.5 1.6Sep. 90 Economic Forecasts (32) — — 0.8 20.1 0.7 1.7Oct. 90 Economic Forecasts (35) — — 0.8 20.7 0.0 2.4Oct. 90 Act. 1st est. 90.3 — — 1.8 — — —Nov. 90 Blue Chip (52) — — — 20.8 20.6 0.8Nov. 90 Economic Forecasts (30) — — — 21.0 20.7 0.6Nov. 90 Act. 2nd est. 90.3 — — 1.7 — — —a Average prediction for 90.1 and 90.2.b Average prediction for 90.3 and 90.4.c Average prediction for 91.1 and 91.2.

from the end of 1989 until the Gulf War began in accurate reflection of these two diverse views andAugust 1990. However, was there a consensus should not be used in an analysis designed to analyze

5among all the forecasters, namely that there would the behavior of forecasters in general . Rather thebe no recession in 1990? This question must be entire distribution of predictions of every surveyanswered before we can generalize about forecasters’ should be examined.failure to predict this recession.

It is conceivable that in any survey half the5Although either the mean (or the median) of a group of forecastsindividuals might have predicted moderate growthhas been called the consensus forecast, Schnader and Stekler

whereas the other half might have forecast a mild (1991) suggested that these central tendency statistics did not trulyrecession. If this were the case, there would be no reflect the absence or presence of a general agreement or‘consensus’, and the mean forecast would not be an consensus.


Table 2Standard deviations of the predictions of the quarterly change in real GNP published in Economic Forecasts, December 1989–November1990

Survey month (20th day) 1990.1 1990.2 1990.3 1990.4 1991.1 1991.2

December 1989 1.13 0.81 0.86 1.00 — —January 1990 1.11 0.82 0.84 0.99 — —February 1990 0.78 0.73 0.61 0.72 — —April 1990 — 0.93 0.75 0.86 0.73 —May 1990 — 0.78 0.75 1.04 0.83 0.57June 1990 — 0.98 0.85 0.70 0.42 0.42July 1990 — 0.63 0.89 0.99 0.77 0.60August 1990 — — 1.27 1.62 1.20 0.94September 1990 — — 0.80 1.67 1.70 1.20October 1990 — — 0.80 1.40 1.76 1.34November 1990 — — — 1.28 1.43 1.37

3.1. Procedures part of the year there may even have been somedecline in the diversity of the outlook for 1990.

Prior studies have used two techniques for measur- The second way of to determine whether thereing the general agreement, or lack thereof, among were disparate views among the forecasters is toforecasters. The first is the standard deviation of the examine the distribution of forecasts. Table 3 indi-forecasts obtained from each survey (Zarnowitz and cates that there were a number of surveys in whichLambros, 1987). The other, developed by Schnader the distribution was skewed and thus did not indicateand Stekler (1991), examines the entire distribution a ‘consensus’. These skewed distributions showedof a cross section of forecasts to determine whether that there was an identifiable minority opinion aboutthere is a ‘consensus’. There is a ‘consensus’ if the the state of the economy, and the direction ofdistribution of forecasts is unimodal, symmetric and skewness indicated how that minority opinion de-relatively peaked. The statistical tests associated with viated. Only in the first and last months of 1990 werethat procedure were applied to the various sets of these distributions skewed to the left, indicating that

61990 forecasts . the minority opinion was one of lower growth rates.This was also true for the Wall Street Journal surveys

3.2. Results of July and August, but not for the predictionsincluded in the June and July Economic Forecasts.

Table 2 presents the standard deviations of the The latter distributions were skewed to the right, i.e.forecasts of the individuals whose predictions were the minority expected higher growth rates.

7reported in Economic Forecasts . There was no The results obtained from both of these ap-substantial increase in the standard deviations of the proaches indicate that on average the mean forecasts,forecasts until July and August. In fact, in the early that were not predicting a recession, can be consid-

ered representative of the views of most forecasters.6This methodology has been used previously by Schnader and This finding is corroborated by counting the numberStekler (1979); Zarnowitz and Lambros (1987); Schnader and of individuals who predicted that real GNP wouldStekler (1991) and Kolb and Stekler (1996).7 decline in particular quarters of 1990–1991 (TableIt would also have been possible to calculate the standard

4). A few individuals foresaw a recession, butdeviations of the Blue Chip annual forecasts, that were predictionsof the growth rates of real GNP for the entire year including the neither the number of individuals expressing thismonths that were already history. These were not pure forecasts view nor the diversity of predictions increased untilsince they included actual data for that part of the year that had August. Until September the prevailing view con-already occurred. Under these circumstances, as the year pro-

tinued to be that the economy would avoid agressed, the standard deviations would be expected to decline, forrecession. While the number of individuals whoa larger portion of the estimates would be based on actual data

available to all individuals. foresaw a recession increased, even as late as


Table 3The absence or presence of a ‘consensus’ in various surveys

Date Source 90.1 90.2 90.3 90.4 91.1 91.2

Dec. 89 Business Week C NC NC NC — —Dec. 89 Economic Forecasts C C NC NC — —Jan. 90 Wall St. Journal NC NC — —Jan. 90 Economic Forecasts C C NC NC — —Feb. 90 Economic Forecasts C C C C — —Apr. 90 Economic Forecasts — C C C C —May 90 Economic Forecasts — C C C C —June 90 Economic Forecasts — C NC C C CJuly 90 Wall St. Journal — — NC NCJuly 90 Economic Forecasts — — C NC NC CAug. 90 Wall St. Journal — — C NCAug. 90 Economic Forecasts — — NC C C CSep. 90 Economic Forecasts — — C C C COct. 90 Economic Forecasts — — C C C CNov. 90 Economic Forecasts — — — C NC C

C indicates a consensus; NC indicates no consensus.

Table 4Number of predictions of negative growth

Date of forecast Source (no. of people) 90.1 90.2 90.3 90.4 91.1 91.2

Dec. 1989 Economic Forecasts (31) 3 3 1 1 — —Dec. 1989 Business Week (50) 5 2 3 1 — —

a bJan. 1990 Wall St. Journal (40) 4 2 — —Jan. 1990 Economic Forecasts (29) 3 0 1 1 — —

b cJuly 1990 Wall St. Journal (40) — — 2 1b cAug. 1990 Wall St. Journal (34) — — 7 8

Aug. 1990 Economic Forecasts (30) — — 5 7 2 0Sept. 1990 Economic Forecasts (32) — — 5 18 11 2Oct. 1990 Economic Forecasts (35) — — 5 21 18 5Nov. 1990 Economic Forecasts (30) — — — 22 19 12a Predictions for 90.1 and 90.2.b Predictions for 90.3 and 90.4.c Predictions for 91.1 and 91.2.

October only a small minority of forecasters recog- 4. Public vs private forecastsnized that the economy was already in a recessionand one third of those surveyed did not even foresee Because forecast evaluations have shown thata recession in the fourth quarter. Thus we can macroeconomic forecasts are biased, models haveconclude that most forecasters not only failed to been developed which postulate that these biasespredict it in advance but also did not identify it result from the rational behavior of forecasterscontemporaneously. These results are in accord with (Ehrbeck and Waldmann, 1996; Laster et al., 1996).the McNees (1992) finding (p. 20) that forecasters These theories postulate that forecasters might notwere slower in recognizing this recession than they reveal their true beliefs in order to influence theirhad been in 1973, 1980 and 1981. clients. Thus they would be willing to issue biased


predictions. All of the individuals whose forecasts real GNP (September issue of Economic Forecasts).were examined worked in the private sector and it The similarity in the forecast patterns of private andcould be argued that this type of behavior motivated Fed forecasters makes it extremely unlikely that the

8them . failure to predict this cyclical turn can be explainedThese private forecasts can be compared with on the basis of rational bias behavior.

predictions made by the staff of the Board ofGovernors of the Federal Reserve for the regularmeetings of the Open Market Committee. These 5. Why the errors?forecasts are included in the Greenbook, a documentprepared for the members of the Open Market There are several possible explanations for thisCommittee. They are not available to the public until general failure to foresee this recession. One view is5 years after the meeting for which they were that the data that were available contemporaneouslyprepared. Presumably, the Fed forecasters, who were ambiguous or erroneous; using this informationwould not need to influence private clients, would individuals might not have been able to foresee the

9not exhibit the same kind of rational bias . If the Fed recession. Another possibility is that this recessionforecasts also failed to predict the 1990 recession, was caused by factors that had not been observed inthese errors could not be attributable to rational bias previous declines and were thus unpredictable. Final-behavior, and other explanations would have to be ly, there is the belief that the forecasting processsought. itself contributed to these errors.

The Fed forecasts show the same pattern as theprivate ones (Table 5), i.e. the Fed forecasts also fail 5.1. Datato predict a downturn prior to the Gulf War. In theAugust 15 Fed prediction, very slow growth was Stock and Watson (1993) discuss the data prob-expected for the second half of 1990, but there was lems in the context of explaining why their newno prediction of a recession. A downturn beginning indexes of leading and coincident series failed toin 1990.4 was forecast on September 26; this was predict the 1990 recession. They show that the seriesalso when a majority of the private forecasters that had usually performed well in anticipatingproduced predictions having negative growth rates in previous recessions failed to do so in 1990. How-

ever, they do note (Stock and Watson, 1993, p. 96)that there were a few series that did perform well in

Table 5 anticipating the recession. Unfortunately they hadForecasts made by economists of the Board of Governors of the

not utilized these series in constructing their in-Federal Reserve: growth rates of real GNPdicators.

Date of forecast % Growth (annual rate) Some of the significant economic data that were90.1 90.2 90.3 90.4 91.1 91.2 available contemporaneously during the middle part

of 1990 are presented in chronological order in four12/13/89 2.1 1.2 1.6 1.6 — —1/31/90 0.7 2.6 1.6 1.7 — — tables in Appendix B. This analysis enables us to3/21/90 2.0 2.6 1.7 1.71 1.7 1.8 determine the extent to which the data might have5/9 /90 — 2.2 2.0 1.8 1.5 1.1 contributed to the failure to predict or identify the6/27/90 — 1.3 1.6 1.6 1.8 2.1

recession. The four subintervals of the time period8/15/90 — — 1.2 0.5 1.3 2.015 May–30 November were selected to approximate-9/26/90 — — 1.4 21.0 20.4 2.0

11/7 /90 — — — 22.1 21.1 1.4 ly conform to the time intervals between the Fedforecasts that are among the inputs for the corre-sponding meetings of the Federal Open Market

8However, Ehrbeck and Waidman’s empirical resuiLts did not Committee. In addition to the statistics, these tablessubstantiate this hypothesis.9 also present the interpretations that analysts andScotese (1994) has, however, suggested that revisions in the Fed

forecasters placed on these data as they becameforecasts were smaller than might have been expected fromrational forecasts. available. The data that became available between


12May 15 and July 2 displayed a mixed pattern and decline . Such a signal would have occurred onlysuggested that the economy was experiencing slow with the release of the September value of the index

13growth but had not yet become recessionary. in early November . Thus, if forecasters would onlyThe data released between early July and August have predicted a recession if there was a downturn in

21 were decidedly more negative. In fact, some the Composite Index of Leading Series, they wouldobservers who commented on data published in early not have predicted this recession until NovemberAugust thought that the US economy might be (This, however, does not explain why a substantial

10entering a recession . By the end of September, the number of forecasters still had not made such adata were clearly negative, but even at this stage not prediction by the middle of November).everyone was willing to predict a recession. The data The second data problem might explain why therereleased in October and November clearly indicate a was such a long delay in identifying the recession.

11decline . At the end of October the Department of CommerceWhen the data that were available contempora- published the preliminary GNP estimates for the

neously are placed in chronological order as they are third quarter of 1990. These numbers showed thatin Appendix B, it is clear that, for the most part, they real GNP had grown at an annual rate of 1.8% in thatshow a progressively weakening economy. In that period. The final figures now available indicate that

14sense, the data were not erroneous or misleading. real GNP had actually declined in that period . InThere were, however, two data anomalies that might other words, the early data showed an economy thathave contributed to the forecasters’ problems. One had appeared to be much stronger than it actuallymight explain why forecasters failed to predict the was.turn; the other may have added to the delay in The evidence clearly indicates that all of the dataidentifying the recession. Both data problems stem available in real time were ‘not’ clearly signalling afrom the fact that the numbers that were available on turn. Undoubtedly, these data problems were con-a ‘contemporaneous’ basis in 1990 differ signifi- tributing factors in both the failure to predict thecantly from the figures that ‘now’ refer to that recession in advance and the inability to recognize itperiod. In other words the early data did not reflect quickly once it occurred. However, there was suffi-the true picture. cient additional evidence that, if recognized and

In order to predict a recession in advance, fore- interpreted correctly, could have led to better predic-casters frequently examine movements in the tions. Moreover, these data issues do not explainComposite Index of Leading Series. In the past, why a number of individuals had still not identifieddeclines in this indicator had always preceded busi- the recession near the end of 1990.ness cycle peaks, and the absence of a decline in theIndex would suggest that a recession was not immi- 5.2. Was the recession unpredictable? Finalnent. The historical values of the 1990 version of this demandIndex show that a peak occurred in May 1990, thusleading the recession by 2 months. However, the data There has been some debate about the proximatethat were available in mid-1990 do not signal a cause of the recession of 1990 (McNees, 1992;

Blanchard, 1993; Hall, 1993; Perry and Schultze,1993). All agree that this recession was not ‘primari-

10In addition to the comments in the tables of Appendix B, note ly driven by a combination of policy changes andVictor Zarnowitz’ interpretations of the data and forecasts in thelate August issue of Economic Forecasts: ‘... economy is con-siderably weaker than most analysts and forecasters had assumed

12only a month ago’. ‘... domestic demand has already weakened to The Index of Coincident Indicators declined prior both to therecessionary levels’. beginning of the recession and the turn in the leading indicator.11 13It was November before the Stock and Watson indicators yielded That index is required to decline for three consecutive monthsa probability of 50% for the hypothesis that a recession had begun before a cyclical downturn is predicted (Vaccara and Zarnowitz,in the prior 2 months. In November the probability that a recession 1977).

14began in that month reached 50% for the first time (Stock and The revision from a positive to a negative growth rate for 1990.3Watson, 1993, pp. 118, 146). did not occur until July 1993.


autoregressive responses by other forces weakening had extremely low priors of a forthcoming slump,total demand’ (Perry and Schultze, 1993, p. 193.) (3) the indicator(s) used by the forecasters did notThere is, however, disagreement about the factors signal a downturn, or (4) people did not understandthat caused consumption to decline as much as it did. the relationship between final demand and downturns

While economists cannot completely explain why and did not calculate the likelihood ratios properly.consumption declined, forecasters realized in late The cost of predicting a recession may have been1989 and early 1990 that consumption would have to too high because in 1989 economists had predicted aremain strong for the economy to avoid a recession recession that failed to materialize. These individuals(Cooper and Madigan, 1989). Thus, when ‘nominal’ did not wish to repeat that mistake. With respect toretail sales during the spring and summer of 1990 the second point, Stekler (1972) had advanced thewere no higher than the January levels, this could hypothesis that downturns are not predicted becausehave been a signal that a recession was impending. forecasters have low priors that such an event willMoreover, Perry and Schultze (1993, p. 149) docu- occur. This hypothesis can be rejected becausemented a relationship that every forecaster should during the previous year’s slow growth, individualshave been aware of. Namely, that final sales always were openly debating whether there would be a hardslow down before peaks, especially if calculated (recession) or soft landing. Moreover, every fore-relative to potential output. caster could observe that there were others who

There is additional evidence that forecasters could were, in fact, predicting a cyclical downturn. On thehave taken into account. In every issue of Economic other hand, if the forecasters were solely relying onForecasts, Victor Zarnowitz commented on and inter- signals from the leading indicators, they would notpreted the latest data and forecasts. In the August have been able to predict the recession untilissue he noted that final sales had declined and that November, but should then have been able todomestic demand had ‘weakened to recessionary identify it quickly.levels’. In a separate statement, he indicated that In the context of how individuals generate fore-

15there was a 50–50 chance of a recession . In his casts, we wish to repeat an observation that wasSeptember commentary, Zarnowitz noted the decline presented above. Economists had noted that a reces-in a consumer confidence index and emphasized the sion could be avoided if consumption were strong:impact that changing expectations could have on

If consumers turn cautious, it would be a bigconsumption. While Zarnowitz is a noted scholar of

blow. A table stands on four legs, says Donald P.business cycles and forecasts, others could have

Hilty of Chrysler Motors Corp., referring to thereached similar conclusions, especially after reading

1990 economy. Ours has only one-consumerhis commentaries.

spending. That’s because far less support will becoming from the other three sources of demand:

5.3. The way individuals make forecastscapital spending, government purchases, and ex-ports (Cooper and Madigan, 1989).

The evidence suggests that while data problemsmight have caused forecasting delays and failures, Similarly, others had assumed that the Fed wouldthe recession was not unforecastable. Some indi- loosen monetary policy in 1990. Consequently, theviduals did predict it in advance and a large number predictions were based on a set of assumptions thatidentified it after its inception. This leaves an un- later may not have been valid. There is, thus, aanswered question: Why did other forecasters not distinct possibility that forecasters did not questionrealize that ‘final demand had weakened to reces- whether the economic data were consistent with theirsionary levels?’ The Schnader–Stekler model, pre- postulated assumptions.sented in the Appendix, provides a number of Finally, there is evidence that forecasters did notpossible explanations: (1) the costs of making a completely understand the dynamic process that theyforecast of recession were too high, (2) individuals were trying to predict. There were comments in the

financial press that a recession could not occur unless15Wall Street Journal, 6 August 1990, p. A2. the Fed first initiated a restrictive policy or that there


could be no recession unless it was first signalled by dividuals did not question whether the observed data16movements in an interest rate spread . We are not was consistent with the assumptions that formed the

the first to advance the hypothesis that forecasters basis of their predictions.did not completely understand the dynamic process Additional research about the forecasting process,that they were trying to predict. In a different bolstered by analyses of predictive failures in othercontext, Fildes and Fitzgerald (1983) had made recessionary periods, is required to further ourexactly the same argument. understanding of these failures. In the meantime, this

study provides some suggestions for avoiding suchpredictive failures in the future.

6. Conclusions Consider the procedures that forecasters, who havea thorough understanding of the way the US

The literature that has evaluated economic fore- economy functions, might adopt. To determine thecasts has shown that most recessions were not likelihood of a recession, they should first examine apredicted in advance. However, there have been few wide variety of data rather than just rely on a smallattempts to explain why this type of predictive number of indicators. Second, in examining thesefailure occurs. We, therefore, examined the forecasts data, those forecasters should always ask whethermade in 1990 in order to determine why most there is any information that is signalling or sug-individuals also failed to predict the recession that gesting that a recession is imminent. In other words,occurred in that year. We first ruled out the possi- they should always be looking for the possibility of ability that these errors were the result of rational bias recession (rather than having an implicit prior of abehavior. recession equal to zero). Finally, these individuals

We then advanced a number of plausible explana- should explicitly state the assumptions that underlietions for these forecasting failures: data problems; the forecasts. They should then track the data andthe recession was not forecastable; the forecasting question whether the underlying assumptions stillprocess was at fault. We indicated that there were seem valid; if not the assumptions and forecastsome data problems that might have contributed to should be revised to account for the changed circum-this predictive failure, especially if forecasters had stances. If the assumptions are revised downward tobeen relying on the Composite Index of Leading reflect a less favorable outlook, there will be anSeries. However, an ex post analysis by Stock and increased likelihood that cyclical peaks will beWatson showed that there were a substantial number predicted. Since the failure to predict these eventsof series that in fact had signalled the possibility of a has been a serious shortcoming of most economicrecession, but these were series that had not been forecasts, the overall value of these predictionswidely used. would, thus, be greatly enhanced.

Although this recession was caused by factors thathad not been observed in some of the immediately

Acknowledgementspreceding recessions, the evidence is that it wasforecastable. This leaves the forecasting process, i.e.

We wish to thank Robert Goldfarb, Steve McNeesthe way individuals prepare their forecasts, as theand two referees for comments on an earlier draft ofprime suspect in this particular predictive failure.this paper.Forecasters could have had low priors about the

possibility of a recession or they might have assignedhigher costs to making false predictions than to Appendix Afailing to predict a turn. There is also the possibility,that in the process of tracking the economy, in- A model for predicting cyclical turns

16 Assume that forecasters use a decision theoreticThese examples should make forecasters wary of relying on aframework in choosing between predicting, (1) asingle theory or indicator as the major or sole technique for

forecasting recessions. turn, H , or (2) no turn in GDP, H . The prediction1 0


would be based on probability distributions which forecast in advance while troughs are predicted inrelate new information, e , to the states of the world, advance, and sometimes even identified too early.k

h and h . It is then possible to calculate the The optimal decision rule for choosing the fore-0 1

likelihood ratio, l , which is the ratio of the cast: H turn is Eq. (1) (Wald, 1950; Green and10 1

probabilities that the information had been generated Swets, 1966):by the two alternative states of the world:

l (e ) 5 p(e /h ) /p(e /h ) l (e ) . [(V 2V ) p(h )] / [(V 2V ) p(h )] (1)10 k k 1 k 0 10 k 00 01 0 11 10 1

The forecaster has prior probabilities, p(h ) and0

p(h ) which were developed before the new in- Otherwise H is chosen.1 0

formation became available, about the two states of It should be noted that the likelihood ratio is anthe world. In addition, values (costs) are assigned to objective measure, for it is a function of real worldthe four possible outcomes: (1) V , forecasting a data. On the other hand, the two ratios on the r.h.s.11

turn and a turn occurs; (2) V , not predicting a turn of Eq. (1) are subjective and forecaster specific. The22

and no turn occurs; (3) V , forecasting a turn that costs to a forecaster could be his reputation, his10

does not occur; and (4) V , not predicting a turn that believability, or merely some other subjective feel-01

occurs. While the fourth outcome is the error that has ing. Thus, the forecaster specific costs associatedbeen reported in the forecast evaluation literature, with the various forecast errors can affect thethere is a definite asymmetry associated with predic- prediction which is issued and thus the possibilitytions of cyclical peaks and troughs. Peaks are not that turns are missed.

Appendix B

Statistics released between 5/15 and 7/2

Date Statistic Effect Interpretation

16 May Industrial output down 0.4% in April Negative Manufacturing sector is slowingCapacity utilization down to 83.5% Mixed Slowing manufacturing, but less Inflationary

pressure17 May CPI-U up 0.2% from March Positive Inflationary pressures may be easing, allowing

the Fed to cut ratesHousing starts down 5.8% Negative Housing sector is in a significant decline, could

spread to other sectors24 May Durable orders down 4.1% Negative Shows a slowing (or flat) manufacturing sector25 May GNP estimate for Q1 1990 cut to 1 /3% Negative Economy is sluggish, growing slower than

thought26 May Personal income up 0.3% Negative Incomes are growing very sluggishly

Personal spending up 0.6% Positive Consumption seems to be fairly strong, but willit last?

Savings fall to 5.5% from 5.8% Mixed People are still consuming, but if savings dwin-dle, might they stop?

30 May Leading indicators fall 0.2% Negative Shows sluggish economy, but rose the monthbefore and does not indicate recession

1 June Factory orders fall 2.3% Negative Manufacturing seems to be weakening2 June Unemployment falls to 5.3% Mixed Business payrolls didn’t expand, and census


works account for much of the drop in un-employment

Manufacturer’s purchasing index up to 50.7 Positive Means manufacturing may yet turn around, butfrom 50.2 below is still on the brink of a contraction

5 June Productivity fell 2.7% in Q1 1990 Negative Means that less is being produced per worker,and will hamper future growth

14 June Retail sales fall 0.7%, 3rd consecutive fall Negative Consumers may be spending less, and willhamper growth

15 June PPI up 0.3% in May Positive Inflation may be coming under controlInventories up 0.1% and sales down 1.1% Negative Large inventories can lead to a production

cutback, and a recession. However, this slightgain does not foreshadow a recession

18 June Factory output up 0.6% Positive Could mean a manufacturing turn-around if itcontinues

CPI up 0.2% Positive Means inflation more and more looking undercontrol

Industrial production up 0.6% Positive Although mostly from auto parts, gives somehope for a rebound in manufacturing

Utilization up (83.6 from 83.3) Positive20 June Housing starts down 1.4% Negative Housing is close, if not in, recession. Further

shows the sluggishness of the economy22 June Final GNP boosted to 1.9% Mixed Better than the revised, but still a bit sluggish.

Further, numbers show higher inventories andweaker consumer spending. Exports, however,show big growth, and could keep economy afloat

Corporate profits down 47% Negative Shows more sluggishness in the economy25 June Durable goods orders up 3.9% Positive Manufacturing may be on the turn around

Personal income up 0.3% Negative Income growth is sluggishPersonal spending flat Negative Spending is worrisome, should be higher

28 June Leading indicators up 0.8% Positive Flip-flopping adds to view of a sluggisheconomy, but no recession. Looks like manufac-turing is on the up, but consumption on thedown

Overall 14 Negative / ten positive / four mixed Overall, seems mixed with more positives lateand a manufacturing turn-around seemingly inthe works. Spending and housing seem to beworrisome, but the economy looks more slug-gish than recessionary



5 July Fact orders up 2.1% in May Positive Could indicate that the manufacturing sector willrebound

6 July Non-farm business productivity down 2.7% in Negative Lagging productivity means a sluggish economy.QII. Manufacturing productivity up 4.1% Manufacturing, however, may be turning up

9 July Unemployment drops 5.2% Positive More people working is good for the economy,but the temporary census workers may be skew-ing the data

Only 40 000 jobs created Negative The economy seems to be growing as fast as canbe afforded


People are spending, which is good. But willMixedConsumer credit up 65% in May10 Julyhigher debt force spending cutbacks down theroad?Inflation seems to be under control, meaning thePositivePPI up 0.2% in June16 JulyFED will not raise ratesRetail sales had been down the last 3 months,PositiveRetail sales up 0.5% in Junebut increased consumption could keep theeconomy aliveRising inventories could mean future productionNegativeBusiness inventories up 0.4%17 JulycutbacksManufacturing and wholesale sales up, but weakMixedTotal sales up 0.9%retail sales mean that the economy may not berebounding

Positive Good on the surface, but mostly due to summerIndustrial output up 0.4% in June18 Julyair conditioning and temporary automatic manu-facturing increases. However, numbers couldmean a fundamental (not temporary) turn inmanufacturing

Capacity utilization up to 83.5% from 83.3% Positive19 July CPI up 0.5% Negative Inflation is still a problem, but maybe it will

subside (low previous PPI)Housing starts down 2.3% in June Negative Housing is in a recession, if not a depression

26 July Durable orders fall 3.2% in June Negative Manufacturing may not be rebounding, but mayjust be volatile figures

30 July GNP slows to 1.2% in QII Negative Economy at a near stand still, perhaps on theverge of recession now

31 July Consumer spending up 1% Positive Strong spending if it lasts, but is it just pent-updemand?

Savings falls to 4.9 from 5.4% Negative People may be spending out of savings, meaningit is temporary

Personal income up 0.4% Negative Income not keeping up with spending2 Aug Leading indicators unchanged Negative Economy could be turning down

Purchasing managers index falls to 47.4 from Negative Manufacturing does not appear to be rebound-51.1 ing, if spending falls the economy may go into

recession3 Aug Factory orders fall 1.5% in June Negative Manufacturing is not rebounding

Durable orders down 2.8% Negative Economy may be on the edge of recession6 Aug Unemployment up to 5.5% Negative This could indicate the beginning of recession,

or be skewed by census workers being layed-off.However, service employment growth, the mainengine of this expansion, slowed

7 Aug Productivity up in QII by 1.9% Mixed Higher than expected, but showed some weaksectors

8 Aug Consumer credit slows to 0.8% increase Negative Had grown 6.1% in May, and may be indicatinga weary consumer

13 Aug PPI declines 0.1% in July Positive Inflationary pressures were easing, but Gulf Warwill re-ignite them

15 Aug Retail sales up 0.1% Negative Consumption is slowing, which may put theeconomy in recession

16 Aug Industrial output is unchanged Negative May indicate the economy has turnedCapacity utilization down to 83.4 from 83.6% Mixed Manufacturing would seem to be in decline, but

lower capacity utilization may hold off theinflation from the Gulf War


If spending picks up, would mean a productionPositiveBusiness inventories fell 0.4%increaseInflation was a problem before the Gulf WarNegativeCPI up 0.4% in July17 AugSixth straight month of declineNegativeHousing starts fell 2.6%

Overall, seems mixed with more negatives late19 Negative /nine positive / four mixedOveralland a manufacturing turn-around seemingly inthe works. Spending and housing seem to beworrisome, but the economy looks more slug-gish than recessionary


InterpretationEffectStatisticDate

While positive, they do not indicate a rebound inPositiveDurable good orders climb 2.9% in July23 AugmanufacturingThis figure is lower than what was expectedNegativeGNP rose by 1.2% in QII in real terms27 AugVery slow growth in spendingNegativePersonal spending up 0.2% in July28 Aug

Negative Savings are low, meaning consumers will notSavings at 4.9%spend if incomes slow

Consumer confidence hits 7-year low Negative Consumers are getting scared that a recessionmay be on its way

30 Aug Leading index unchanged in July Mixed Means no recession predicted, but also nogrowth predicted

31 Aug Factory orders increase 1.6% in July Positive Any increase in these statistics is positive,however is it enough?

Orders for durable up 2.8% Positive5 Sept Purchasing managers index to 47% Negative Shows manufacturing sector in decline10 Sept Jobless rate climbs to 5.6% in August Negative Could indicate a turn has occurred, could just be

skewed17 Sept PPI increases 1.3% from previous month Negative The oil crisis is beginning to have an impact

Retail sales fall 0.6% Negative Consumers appear to be spending lessIndustrial output down 0.2% Negative Output is decreasing, could indicate economy

has turned18 Sept Inventories rose 0.7% in July Negative Could mean production cut backs if this con-

tinuesSales fall 0.5% Negative Consumers appear to be spending lessInventory:sales ratio at 1.49 from 1.47 Negative

20 Sept Housing starts down 1.7% Negative Seventh consecutive month of decline26 Sept QII GNP revised to 0.4% Negative Economy was moving quite slow last quarter27 Sept Durable goods orders fall 0.8% in Aug Negative Manufacturing is clearly slowing

Personal income down 0.3% Negative Incomes are decreasing—consumers are in trou-ble

Personal spending down 0.1% Negative Not only did spending fall, it did not keep upwith income

1 Oct Leading indicators fell 1.2% in August Negative Still not predicting a recession, but not a goodsign

2 Oct Purchasing managers index down to 44.7% Negative Shows manufacturing in decline, but economy ingeneral is not

Overall 19 Negative / three positive /one mixed The economy is clearly in trouble. While it maybe too early to officially say it’s a recession, it isin close




4 Oct Factory orders climb 1.8% Positive Maybe the economy will turn, but probably notsignificant

Orders for durable fell 0.5% Negative Manufacturing continues its weakness8 Oct Unemployment up to 5.7 from 5.6% Negative Could be further evidence a recession is under-

way. Further, the economy has created only70 000 jobs in 3 months

10 Oct Credit increased 3.4% Positive Again, consumers seem to be holding back onspending so this would just show how far in debtthey are

15 Oct PPI increases 1.6% in September Negative The Gulf Crisis is taking its toll on inflation. TheFed may have no choice but to keep policy tight

Retail sales up 11% Positive A good increase, but not adjusted for inflation16 Oct Inventories up slightly (0.5%) Negative Inventories have been edging up in the last

monthsTotal business sales up 2.1% Positive Good in general, but retail and manufacturing

show weakness18 Oct Industrial production up 0.2% Positive Economy showing a few signs of life, but is it

enough?Housing starts fall 0.6% Negative Housing shows no signs of lifeCapacity utilization was unchanged Mixed Would have been nice if it had increased. Could

point to a mild recession19 Oct CPI up 0.8%, puts inflation at 6.2% Negative This is fairly significant, numbers show neo-

1970s stagflation25 Oct Durable orders fall 1.7% Negative These decreases are becoming more significant31 Oct GNP for QIII put at 1.8% Mixed Doesn’t dampen any recession fears1 Nov Personal spending up 0.3% Negative Slow increase, and outpaced income growth2 Nov Purchasing managers index falls to 43.4% Negative Indicates the economy is in recession

Help wanted advertising index hits 5 year low Negative The labor market looks to be in a slump5 Nov Unemployment holds at 5.7% Mixed Could be worse, but payrolls declined for the 3rd

monthLeading indicators fall 0.8% Negative After revisions, index is not predicting a turn.

12 Nov PPI up 1.1% Negative The core rate appears minimal, but the oil crisisis causing significant inflationary problems

14 Nov Industrial output declines 0.8% Negative This could be it, the economy is now in reces-sion

15 Nov Retail sales up 0.1% before inflation Negative Slow growth, or actual decline, will not turn theeconomy back towards growth

16 Nov Consumer confidence down 24.3 points in 3 Negative Consumers think that the economy is in reces-months sion

19Nov CPI Increases 0.6% Negative The inflation rate is now at 8.9% at annual rates21 Nov Housing starts down 6% Negative Ninth straight decline30 Nov Personal income up 0.1% Negative The recession is taking a toll on consumers

Personal spending is flat Negative Spending was actually lower than income, firsttime in months

After tax income down, after inflation, 0.5% Negative Shows that consumers are being pinched bydeclining incomes

Overall 20 Negative /five positive /3 mixed The economy is clearly in recession. The lasttwo periods have shown significant decline


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