Discussion Paper No. 513

JAPANESE GDP FORECASTERS ARE
PESSIMISTIC IN BOOM,
OPTIMISTIC IN RECESSION,AND ALWAYS TOO JUMPY

Masahiro Ashiya

June 2000

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Japanese GDP Forecasters Are Pessimistic in Boom,
Optimistic in Recession, and Always Too Jumpy
Masahiro ASHIYA
June 2000

This paper analyzes the rationality of Japanese macroeconomic forecasters. It finds thatJapanese individual forecasters are pessimistic in boom and optimistic in recession, andthat they over-react to new information. Across forecasters, the magnitude of average
forecast revisions is not correlated with the magnitude of average forecast errors. These
results together are consistent with neither the rational expectations hypothesis norreputation models with rational and strategic forecasters.
Keywords: Rational expectation; Reputation; Over-reaction; Forecast.
JEL Classification Codes: C53, D84, D82.

1. Introduction
It is now one of the hottest issues in Japan whether Japanese economy is on the way torecover from the long recession. Many economists release forecasts of the Japanese real
GDP growth rate, most of which are positive for fiscal year 2000. Recent research infinancial economics, however, has shown that economic forecasts are subject tobehavioral biases. De Bondt and Thaler (1990) find that security analysts’ earnings
forecasts are too optimistic and too extreme. Abarbanell and Bernard (1992) show that
security analysts under-react to recent actual earnings. Amir and Ganzach (1998) showthat analysts’ forecast changes (i.e. earnings forecasts for the current year minus actual
earnings in the previous year) tend to over-react and that their forecast revisions (i.e.
new earning forecasts minus previous forecasts for the current year) tend to under-react.
Ehrbeck and Waldmann (1996) find that forecast revisions of short-term interest rates
have a tendency towards over-reaction. Since the same biases may affect Japanese
economists, their forecasts need be carefully interpreted. This paper is the first attempt
to investigate the effects of these biases on the GDP forecasts.
The paper is organized as follows. Section 2 explains the methodology. Section 3
explains data, and Section 4 reports the results. It finds that Japanese individual
forecasters are pessimistic in boom and optimistic in recession. Furthermore, it findsthat their forecast revisions have a strong tendency towards over-reaction. These arenegative evidence against the rational expectations hypothesis. Section 5 tests whether
reputation models can rationalize these results. When each forecaster’s ability is private
information, rational forecasters mimic what able forecasters will do. Thus forecasters
over-react if and only if abler forecasters tend to do so. Consequently, if concern forreputation is the main reason for over-reaction, forecasters who change their forecasts
by a large amount ought to have small forecast errors on average. The cross-sectional
analysis in Section 5, however, finds that there is no correlation between each
forecaster’s mean squared forecast revision and mean squared forecast error. Reputation
models cannot explain this result and the biases found in Section 4 together. Section 6
concludes.

2. Methodology

1

We follow Amir and Ganzach (1998) and consider two factors that influence forecast
accuracy. If forecasters are optimistic, their forecast errors tend to be positive. If
forecasters over-react to new information, their forecast errors tend to be positive(negative) when they obtain good (bad) news, i.e. in boom (recession). Therefore thejoint effect of optimism and over-reaction on forecast errors is positive in boom andindeterminate in recession. Similarly, the joint effect of optimism and under-reaction on
forecast errors is indeterminate in boom and positive in recession. Table 1 depicts theseand other cases.
Table 1 reveals three important points. First, a positive forecast error does notnecessarily indicate optimism. The reason is that pessimism plus over-reaction (underreaction) may cause it when the forecast revision is positive (negative). Secondly, theset of a positive forecast revision and a negative forecast error needs not imply underreaction. Over-reaction plus pessimism may cause it. Thirdly, the sign of the forecast
revision is important when we investigate the joint effect of optimism/pessimism andover-/under-reaction. The analysis below divides the data into two sub-samples
according to the sign of forecast revision.
In order to distinguish the effect of optimism/pessimism from the effect of over/under-reaction, forecast errors are regressed on forecast revisions. Define f i t − 2,t as
forecaster i’s initial forecast for year t in year t − 2 , f i t −1,t as i’s revised forecast foryear t in year t − 1 , and g t as the actual growth rate of Japanese real GDP in year t.
Then FEit ≡ f i t −1,t − g t is i’s forecast error for year t, and FRit ≡ f i t −1,t − f i t − 2,t is i’sforecast revision for year t. The regression is(1)FEit = α + β ⋅FRit + uit(Ehrbeck and Waldmann (1996) and Amir and Ganzach (1998) use the same equation). 1
The null hypothesis of rationality is α = β = 0 . Positive α implies optimism, while
negative α implies pessimism. Positive (negative) β implies over-reaction (underreaction) to new information.
When we test the above null hypothesis, we must take account of error correlation
across forecasters. Keane and Runkle (1990) and Ehrbeck and Waldmann (1996) argue
that shocks to the aggregate economy produce forecast errors that are correlated across2

forecasters. Hence we estimate the variance-covariance matrix in the same way asEhrbeck and Waldmann (1996). The estimated matrix V isV = (X ′ X )−1(2)(∑ [(∑ X uˆ )(′ ∑ X uˆ )])(X ′ X )TNtit ti i

Nt ti ii

−1

where X it is (1, FRit ) if FRit is available and (0, 0 ) otherwise, X is the TN × 2 -stackof X it , and ûit is the residual. Ehrbeck and Waldmann (1996, p.31) point out that “theresulting estimate of the variance-covariance matrix of beta [i.e. V] is unbiased underthe null of rational expectations and a quadratic loss function. On the other hand, if
forecast errors are predictable, the resulting estimate will be biased upward by a positive
definite matrix. … They provide extremely robust tests with extremely low power.”

3. Data
Toyo Keizai Inc. has published the forecasts of about 80 Japanese economists in theJanuary or February issue of “Monthly Statistics (Tokei Geppo)” since 1987. 2 Eacheconomist makes forecasts of the Japanese real GDP growth rate for the ongoing fiscal
year and that for the next fiscal year. For example, January 1990 issue contains forecasts
for fiscal year 1989 (from April 1989 to March 1990) and fiscal year 1990 (from April
1990 to March 1991). We treat the former as f i t −1,t and the latter as f i t −1,t +1 . We
exclude the forecasts for fiscal years 1987 ( f i t − 2,t is missing) and 2000 ( g t is missing),and use the forecasts for fiscal years 1988 to 1999. We exclude economists whoparticipate in less than four consecutive surveys, leaving 79 economists. The total
number of forecast sets ( ( f i t −2,t , f i t −1,t )) is 596, and the average number of observations
per economist is 7.54.
Among them, the forecast revision is positive in 210 observations, zero in 37
observations, and negative in 349 observations. We split the full sample into two
subgroups, FRit ≥ 0 and FRit ≤ 0 . The observations with FRit = 0 are classified
according to the sign of the average forecast revision for year t. The subgroup ofFRit ≥ 0 consists of 224 observations, while the subgroup of FRit ≤ 0 consists of 372
observations (Table A1 shows the summary statistics). 3
As for the actual growth rate g t , Keane and Runkle (1990) argue that the revised3

data introduces a systematic bias because the extent of revision is unpredictable for theforecasters. For this reason we use the initial announcement of Japanese government
usually released in June.

4. Results
First we check the relation between forecast revisions and forecast errors. Table 2 showsthat, although there is no bias in the full sample, forecast errors tend to be negative(positive) when forecast revisions are positive (negative). These results appear to (a)indicate under-reaction and (b) deny either optimism or pessimism, but Table 1 inSection 2 has shown the counter examples. Hence we use regression analysis below.
Table 3 summarizes the results of equation (1). The first row is the estimates of thepooled data. The second and the third rows are the estimates of the sub-samples withpositive forecast revisions ( FRit > 0 ) and negative forecast revisions ( FRit < 0 )respectively. OLS estimates of standard errors are in the upper parentheses, and themodified standard errors calculated by equation (2) are in the lower parentheses. Notethat the actual standard errors lie between them.
Table 3 demonstrates strong pessimism and strong over-reaction when the forecast
revision is positive. Both coefficients are significant even if we use the over-estimatedstandard errors calculated by equation (2). On the other hand, Table 3 indicates strong
optimism and strong over-reaction when the forecast revision is negative. α becomesinsignificant but β remains significant when we use the over-estimated standard errors.
4Overall, the regression results of equation (1) clearly reject the rational expectations

hypothesis.

5. Rationality
Section 4 has shown that Japanese economic forecasters are jumpy in both boom andrecession. An open question is whether they over-react for strategic reasons. To address
this issue, let us consider the simple reputation model Ehrbeck and Waldmann (1996)
present. Forecasters privately obtain signals about the growth rate of the ongoing year in
March and August, and they make forecasts after receiving each signal. The quality ofthe signal received in March is identical for all forecasters, but the quality of the signal

4

received in August varies across forecasters. Each forecaster’s ability is private
information.
In this model, abler forecasters attach larger weight on the second signal and thus
make larger forecast revisions in August. Then (a) rational forecasters revise theirforecast excessively in August to make them look able, but among them (b)incompetent forecasters make smaller revisions on average because they cannot rely on
their low quality signals received in August. Consequently, the magnitude of average
forecast revisions will be negatively correlated with the magnitude of average forecast
errors across forecasters (We can obtain the same conclusion from other reputation
models because rational forecasters have an incentive to over-react if and only if abler
forecasters tend to over-react).
We test the above implication by the following regression:(3)

FE i = α + β ⋅ FR i + ui

where FE i ( FR i ) is the mean squared forecast error (forecast revision) of forecaster i.
Table 4 reports the OLS estimates of this regression. It also reports the rank correlation
coefficient of FE i and FR i since ui in equation (3) is not normally distributed.
As shown in Table 4, there is little correlation between FE i and FR i in either
sample. 5 It indicates that forecasters’ strategic behaviors are not the cause of overreaction found in Section 4.

6. Conclusions
This paper investigated the GDP forecast data of Japanese individual economists, andobtained the following results. First, they are pessimistic in boom and optimistic inrecession. Secondly, they revise their forecasts excessively. Thirdly, there is no relation
between the magnitude of average forecast errors and the magnitude of average forecast
revisions. Since neither the rational expectations hypothesis nor reputation models canaccount for these results consistently, it lends considerable support for behavioral
explanations.

5Notes
1. De Bondt and Thaler (1990) and Amir and Ganzach (1998) analyze the relation
between forecast changes and forecast errors. When we apply their analysis to GDP
forecasts, the equation becomesf i t −1,t − g t = α + β( f i t −1,t − g t −1 )where g t −1 is the actual growth rate in year t − 1 . However,∀if i t −1,t − g t = g t −1 − g t + ( f i t −1,t − g t −1 )for given year t. Therefore the estimated coefficients have little theoretical meaning if
the number of forecaster is large relative to the time-series dimension (β will bepositive unless the data contains sufficient number of observations such that theabsolute value of f i t −1,t − g t −1 is large and ( f i t −1,t − g t )( f i t −1,t − g t −1 ) < 0 ). The sameproblem occurs when we replace g t −1 with the average of f i t −2,t as Ehrbeck andWaldmann (1996) do.
2. Ashiya and Doi (forthcoming) also use this data, and investigate the relation between
economists’ age and the degree of herding.
3. We obtain similar results when we exclude the observations with FRit = 0 from thedata.
4. Ehrbeck and Waldmann (1996) find over-reaction but do not find either optimism or
pessimism in the U.S. bond market (They do not investigate the divided data). Amirand Ganzach (1998) investigate the earnings forecasts and find (a) optimism andunder-reaction in the pooled data, (b) over-reaction in the sub-sample of FRit > 0 ,and (c) optimism and strong under-reaction in the sub-sample of FRit < 0 . One
reason why only security analysts in the sub-sample of FRit < 0 tend to under-reactis that, when they receive negative information, they have an incentive to shade theirforecasts to retain good relation with company management (Francis and Philbrick(1993) find evidence that supports this argument). Of course, GDP forecasters in oursample are free from such pressures.
5. Ehrbeck and Waldmann (1996) find positive correlation between the mean squared
forecast revisions and the mean squared forecast errors.

6References
Abarbanell, J.S., and Victor L. Bernard, “Tests of Analysts’ Overreaction/Underreaction
to Earnings Information as an Explanation for Anormalous Stock Price Behavior.”
Journal of Finance, July 1992, 47(3), pp.1181-1207.
Amir, Eli, and Yoav Ganzach, “Overreaction and Underreaction in Analysts’ Forecasts.”
Journal of Economic Behavior and Organization, November 1998, 37(3), pp.333347.
Ashiya, Masahiro, and Takero Doi, “Herd Behavior of Japanese Economists.” Journalof Economic Behavior and Organization, forthcoming.
De Bondt, Werner F. M., and Richard H. Thaler, “Do Security Analysts Overreact?”
American Economic Review Papers and Proceedings, May 1990, 80(2), pp.52-57.
Ehrbeck, Tilman, and Robert Waldmann, “Why Are Professional Forecasters Biased?
Agency Versus Behavioral Explanations.” Quarterly Journal of Economics,
February 1996, 111(1), pp.21-40.
Francis, Jennifer, and Donna Philbrick, “Analysts’ Decisions as Products of a MultiTask Environment.” Journal of Accounting Research, Autumn 1993, 31(2), pp.216230.
Keane, Michael P., and David E. Runkle, “Testing the Rationality of Price Forecasts:
New Evidence from Panel Data.” American Economic Review, September 1990,80(4), pp.714-735.
Toyo Keizai Inc. Monthly Statistics (Tokei Geppo).

7

Table 1. The joint effect of optimism (pessimism) and overreaction (under-reaction) on forecast errors.

Optimism &Positive revision

Negative revision

EffectEffect+Over-reaction+

Optimism &+Under-reaction−

Pessimism &−Over-reaction+

Pessimism &−Under-reaction

Joint effect+?

+−−+−−−+8?

−+?

Joint effect+−?

Table 2: The outcome of forecast errors
FEit < 0FEit = 0

FEit > 0Total

Full sample

26472260596

FRit > 0126

2672224

FRit < 013846188372

Note: FEit ≡ f i t −1,t − g t and FRit ≡ f i t −1,t − f i t − 2,t9Table 3: The effect of forecast revision ( FRit ) on forecast error ( FEit )Model: FEit = α + β FRit
Full sample

α (s.e.)0.038 (0.039) a(0.223) b

β (s.e.)− 0.012 (0.023) a(0.064) b

R2
0.000

Obs.
596

FRit > 0− 0.376 (0.063)*** a(0.215)** b

0.224 (0.061)*** a(0.088)*** b0.053

224FRit < 00.419 (0.080)*** a(0.502) b

0.154 (0.041)*** a(0.115)* b

0.034

372

Notes
a: OLS estimates.
b: Calculated without imposing restrictions on the variance-covariance matrix of forecast errors, except that forecasterrors at different times are assumed to be uncorrelated.
***: Significant at the 0.01 level.
**: Significant at the 0.05 level.
*: Significant at the 0.10 level.

10Table 4: Cross-sectional effect of average forecast revision
Model: FE i = α + β ⋅ FR i

where FE i ≡ avg(FEit ) and FR i ≡ avg(FRit )22Full sample

α (s.e.)0.838 (0.078) b

β (s.e.)− 0.023 (0.025) b

R20.000

FRit > 00.400 (0.070) b− 0.016 (0.048) b

0.000

0.096

FRit < 01.091 (0.092) b− 0.024 (0.021) b0.003− 0.118

Rank correlation a− 0.031

Notes
a: Rank correlation is obtained from a separate regression replacing the variables with their ranks.
b: OLS estimates.

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Table A1: Summary statistics
Full sample

FRit > 0FRit < 0Avg. of FRit

− 0.6279

0.8411− 1.5124

S.D. of FRit

1.5476

0.61571.2358

Avg. of FEit

0.0453− 0.1879

0.1858

S.D. of FEit

0.87890.5737

0.9934

Observations

596

224

37212