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Information Rigidity in Growth Forecasts:
Some Cross-Country Evidence
Prakash Loungani, Herman Stekler
and Natalia Tamirisa
WP/11/125
© 2011 International Monetary Fund WP/11/125
IMF Working Paper
Research Department
Information Rigidity in Growth Forecasts: Some Cross-Country Evidence
Prepared by Prakash Loungani, Herman Stekler and Natalia Tamirisa
1
May 2011
Abstract
This Working Paper should not be reported as representing the views of the IMF.
The views expressed in this Working Paper are those of the author(s) and do not necessarily represent
those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are
published to elicit comments and to further debate.
JEL Classification Numbers:
C53, E37
Keywords:
Information rigidity; forecasts; recessions; crises
Authors‘ E-Mail Addresses:
ploungani@imf.org; hstekler@gwu.edu; ntamirisa@imf.org
1
We are grateful to Olivier Coibion, Frank Diebold, David Romer and Ken West for extensive comments on earlier
versions of this paper. We also thank seminar participants at the IMF, George Washington University‘s Research
Program in Forecasting series and the International Symposium of Forecasting 2010 (San Diego), particularly Chris
Crowe, Neil Ericsson, Fred Joutz, Gian Maria Milesi-Ferretti, Tara Sinclair and Allan Timmerman. Angela Espiritu
and Jair Rodriguez provided outstanding research assistance.
We document information rigidity in forecasts for real GDP growth in 46 countries over the
past two decades. We investigate: (i) if rigidities are lower around turning points in the
economy, such as in times of recessions and crises; (ii) if rigidities differ across countries,
particularly between advanced countries and emerging markets; and (iii) how quickly
forecasters incorporate news about growth in other countries into their growth forecasts, with a
focus on how advanced countries‘ growth forecasts incorporate news about emerging market
growth and vice versa.
Contents Pages
I. Introduction ......................................................................................................... 4
II. Data on Consensus Forecasts .............................................................................. 5
III. Two Tests of Information Rigidity ..................................................................... 7
IV. Information Rigidity in Recessions and Crises ................................................. 10
A. Descriptive Evidence .................................................................................. 10
B. Statistical Tests ........................................................................................... 11
V. Cross-Country Linkages in Forecast Revisions ................................................ 12
A. Statistical Framework ................................................................................. 12
B. Evidence ...................................................................................................... 14
VI. Conclusions ....................................................................................................... 15
Tables
1. Descriptive Statistics ......................................................................................... 18
2. Absolute Forecast Errors................................................................................... 18
3. Informational Rigidities: Tests Based on Forecast Errors (6-month horizon) .. 19
4. Informational Rigidities: Tests Based on Forecast Errors (3-month horizon) .. 20
5. Informational Rigidities in Forecast Revisions................................................. 21
6. Informational Rigidities during Recessions ...................................................... 22
7. Informational Rigidities during Banking Crises ............................................... 23
8. Variance Decompositions ................................................................................. 24
Figures
1. Distributions of Actual and Forecasted Real GDP Growth, 1889-2008 ........... 25
2. Actual and Forecasted Real GDP Growth during Recessions .......................... 26
3. Actual and Forecasted Real GDP Growth during Banking Crises ................... 26
4. Generalized Impulse Responses of Forecast Revisions (Own-country responses;
in percentage points) ......................................................................................... 27
5. Generalized Impulse Responses of Forecast Revisions (Response to Chinese
Revisions; in Percentage Points) ....................................................................... 28
6. Speed of Absorption of News ........................................................................... 29
Appendices
I Description of Sample ...................................................................................... 30
Table 1: List of Countries, Recessions and Crisis Episodes ............................. 30
II Frequency of Data ............................................................................................. 32
III Generalized Impulse Responses of Forecast Revisions .................................... 33
Figure1A. Generalized Impulse Responses of U.S. Forecast Revisions
(In Percentage Points) ...................................................................................... 33
Figure1B. Generalized Impulse Responses of Germany’s Forecast Revisions
(In Percentage Points) ...................................................................................... 34
Figure1C. Generalized Impulse Responses of Japan’s Forecast Revisions
(In Percentage Points) ...................................................................................... 35
Figure1D. Generalized Impulse Responses of Brazil’s Forecast Revisions
(In Percentage Points) ...................................................................................... 36
Contents Pages
Figure1E. Generalized Impulse Responses of China’s Forecast Revisions
(In Percentage Points) ..................................................................................... 37
Figure1F. Generalized Impulse Responses of India’s Forecast Revisions
(In Percentage Points) ...................................................................................... 38
Figure1G. Generalized Impulse Responses of Russia’s Forecast Revisions
(In Percentage Points) ...................................................................................... 39
References .......................................................................................................................... 40
4
I. Introduction
This paper studies the properties of forecasts of real GDP growth for 46 economies
over the past two decades. The source of the data is Consensus Forecasts, which covers both
advanced economies (the label used by the IMF‘s World Economic Outlook to refer to high-
income economies) and major emerging market economies. The structure of the data—
forecasters provide monthly or bimonthly updates of their forecasts of a fixed event (viz.
annual real GDP growth)—allows for a simple way to document the sluggishness with which
news is absorbed into growth forecasts. And the wide country coverage allows us to look at
differences in information rigidities between advanced and emerging market groups, and at
linkages between forecast revisions in advanced countries and emerging market economies.
Our main findings are as follows. First, there is considerable sluggishness in revisions
of growth forecasts. This is consistent with the sticky information models of Mankiw and
Reis (2002), the imperfect information models of Woodford (2002) and Sims (2003), and
behavioral explanations for forecast smoothing (Nordhaus 1987, Nordhaus and Durlauf,
1984, Fildes and Stekler, 2002).
Second, the sluggishness in forecast revisions declines during recessions and banking
crises. We find that forecasts in the year preceding a year of recession start to depart from the
unconditional mean, and the pace of revision picks over the course of the year of the
recession. A similar pattern holds for banking crises. These finding supports models with
state-dependent acquisition of information (e.g. Gorodnichenko 2008).
Third, we confirm the finding of sluggish adjustment in a multivariate setting, by
estimating a seven-country VAR model for forecast revisions. The seven economies are the
so-called G-3 (U.S., Germany, Japan) and the BRICs (Brazil, Russia, India, China)
Forecasters are somewhat slower in absorbing news from other countries than own-country
(or domestic) news. Forecasts for non-U.S countries, particularly those for Germany and
Japan, are generally slow to absorb news from the U.S. There is also a tendency to absorb
news from China at a very sluggish pace.
5
The rest of the paper is organized as follows. The next section describes the structure
of the Consensus Forecasts dataset. Section III presents our basic evidence on the extent of
information rigidity. Section IV documents how this rigidity is attenuated during recessions
and banking crises. Section V looks linkages across countries in forecast revisions. The last
section concludes.
II. DATA ON CONSENSUS FORECASTS
The data set consists of the consensus (the simple average) of analysts‘ monthly
forecasts of output growth for the current and next year for the period from October 1989 to
December 2008. Forty six countries are represented in the sample, of which 15 are advanced
economies (henceforth referred to as AE) and 31 are emerging market economies (EM).
2
The
sample is geographically diverse, covering countries in Africa, Asia, Europe, Middle East
and Western Hemisphere. The full list of countries and regional classifications are shown in
Table 1 of the Appendix.
The forecast data have been collected and published on a monthly basis by Consensus
Economics, Inc. since October 1989 for major advanced economies under the title of
Consensus Forecasts. Over time the data set was expanded to include many emerging and
developing economies, initially on a bi-monthly basis, in a series of related publications.
3
The
frequency of forecasts for many emerging economies increased over the years from bi-
monthly to monthly.
2
Some of the economies are better classified as ‗developing‘ but for convenience we refer to the entire group as
emerging market economies.
3
Latin American Consensus Forecasts have been published on a bi-monthly basis since 1993, Asia Pacific
Consensus Forecasts on a monthly basis since 1995, and Eastern Europe Consensus Forecasts on a bi-monthly
basis since 1998.
6
The event being forecast is annual average real GDP growth. Every month a new
forecast is made of the event.
4
For each year, the sequence of forecasts is the 24 forecasts
made between January of the previous year and December of the year in question. We index
the sequence of forecasts by h, with the January previous year forecast being 24 and the
forecast in December of the current year being 1, In addition to consensus forecasts, the data
set includes actual real GDP growth from the International Monetary Fund‘s International
Financial Statistics.
Using quarterly GDP series, recession episodes are identified based on the classical
definition of a business cycle using quarterly changes in the level of real GDP (Burns and
Mitchell, 1946).
5
Economies are classified as being in a recession in a given month if they
were in a recession in the respective quarter of the year. Note that, on average, actual growth
is not negative during recessions in advanced economies because the dating of recession
episodes is based on the quarterly data and annual growth tends to remains positive during
many recessions. In all, the data set includes 45 recession episodes in advanced economies
and 61 in emerging and developing economies. See Appendix Table 1 for the list of recession
episodes. The dates of banking crises are taken from the Laeven and Valencia (2008)
database, extended to 2010.
6
The sample includes 7 banking crises in advanced economies
and 22 banking crises in emerging markets. These episodes are listed in Appendix Table 1.
Table 1 presents the basic statistics on growth forecasts.
7
Forecast errors are defined
as actual minus forecast, so a negative number indicates overprediction of the growth
4
For some emerging markets, only bi-monthly data are available over some time periods. In these cases we use
the preceding months forecasts to fill in the missing values. See Appendix Table 2 for the list of countries with
monthly and bi-monthly forecasts.
5
See Claessens, Kose and Terrones (2008) for a discussion of business cycle dating in advanced economies.
6
A systemic banking crisis is defined as an event when a country‘s corporate and financial sectors experience a
large number of defaults and financial institutions and corporations face great difficulties repaying contracts on
time. As a result, nonperforming loans increase sharply and all or most of the aggregate banking system capital
is exhausted.
7
The actual values in this paper are defined as the latest available data (as of June 2009). Use of first published
or real-time data would be a useful cross-check, but such data are not always available or easy to collect for a
(continued…)
7
outcome. Averaged over all countries and all forecast horizons, the mean forecast error is
essentially zero; this is also the case for the AE and EM groups separately. The mean
absolute forecast error is 1.7 percentage points. While absolute forecast errors are higher in
the EM than in the AE group, the higher mean and volatility of the EM group must be kept in
mind when judging this performance. The remainder of the table presents similar statistics
for recession and banking crises episodes. Not surprisingly, forecast errors are higher during
these episodes; growth is overpredicted about by 2½ percentage points in recessions and
about 4½ percentage points during banking crises. The overprediction is much larger for the
EM group than for the AE group. The same holds for the absolute forecast errors.
Table 2 shows regressions of the absolute forecast errors on the recession (or bank
crises) dummies and on a variable indexing the horizon. In both groups of economies,
absolute forecast errors become smaller as the forecasting horizon draws to a close. The table
also shows that, as suggested by Table 1, that errors are higher for recession and banking
crises episodes than for other years.
III. TWO TESTS OF INFORMATION RIGIDITY
We conduct two statistical tests to document the extent of informational rigidity. The
first, following Coibion and Gorodnichenko (2010), is to regress the forecast error,
,,t h t h
AF
,
on the forecast revision,
,t h k
r
:
*
, , , ,t h t h t h k t h
A F r e
(1)
where t is the target year, h the forecast horizon and
1k
.
Coibion and Gorodnichenko
show that the coefficient on the forecast revision is zero under the null of full information
rational expectations, whereas a positive value indicates information rigidities. One feature of
this test is that it requires the use of the growth outcomes and hence requires a view on
whether to use the latest data or an earlier vintage. Our test uses the latest data, but it would
be useful to use earlier vintages as a check on the results.
large group of countries. As noted later, many of our statistical tests do not require use of the growth outcomes
and hence are able to sidestep the issue of which vintage of the actual data to use.
8
Tables 3 and 4 present the results of this test; in the former table, the revisions are
over a six-month horizon and in the latter over a three-month horizon. Each table reports five
regressions, each corresponding to the forecast for the selected month, viz., September of the
previous year, and March, June, September and December of the current year. So, for
example, the first column shows a regression of the forecast error made in September of the
previous year on the revision between March and September of the previous year, while the
last column shows a regression of the forecast error in December of the current year on the
revision between June and December of the current year.
The results in Table 3 can be summarized as follows. First, the coefficient estimates
are almost all positive and significantly different from zero. Hence the null of full
information rational expectations can be rejected, and the rejection goes in the direction
consistent with models with information rigidity. The estimated coefficients in the first
column imply that, in the context of sticky information models, agents update their
information sets every 5 ½ to 7 months. Second, the magnitude of the coefficients declines
monotonically in going from column 1 to column 5. Hence, while the evidence in favor of
information rigidities remains strong, there is somewhat quicker updating of information as
the forecasting horizon draws to the a close. This is particularly the case for advanced
economies; in the regression in the fifth column for example, the null of full information
rational expectation cannot be rejected for this group. Third, with the exception just noted,
there is not much difference between the coefficients for the advanced and emerging country
groups. The coefficients for the latter tend to be higher than for the former, suggesting
greater information rigidities in emerging markets; but in economic terms (i.e. in the implied
estimate of how it takes agents to update their information sets), the differences do not seem
very significant—the updating of information takes about 1 to 2 months longer in emerging
markets group.
Table 4 presents a similar set of regressions, except that the horizon over which the
revisions are made is now three months. Compared with the corresponding regressions in
Table 2, the coefficients estimates are larger, though again in most cases the difference in
economic terms in not very significant. For the case of advanced countries, the decline in
9
coefficient estimates as the forecasting horizon draws to a close is again monotonic, with the
estimate in the fifth column consistent with full information rational expectations. In the case
of emerging economies, the pattern is choppier; and, as in Table 2, for this group there is
evidence in favor of information rigidities persisting even at the end of the forecasting
horizon.
The second test of information rigidity exploits the fact that we have a sequence of
forecasts of forecast for the same fixed event, viz., annual real GDP growth. Under the null
of full information rational expectations, this sequence of forecasts must follow a martingale
(Nordhaus, 1987). To implement the test, we run regressions of the forecast revision, r
i,t,h ,
on
past forecast revisions:
, 0 1 , ,
*
t h t h k t h
r r u
(2)
As before, t the target year, h the forecast horizon and
1k
.
If
1
= 0, there is no
informational rigidity in forecasts. Note that the implementation of the test does involve use
of the actual growth outcomes and hence side-steps the issue of what vintage of the actual
data to use (revised data vs. preliminary release of the data).
In Table 4 we again present results for a variety of different forecast horizons as a
way of testing the robustness of our results. In the first column, the dependent variable is the
revision in the forecast between September and March of the current year. The independent
variables are the revision between March of the current year and September of the previous
year (―lag 1‖) and the revision between September and March of the previous year (―lag 2‖).
As shown, there is a strong positive correlation between the current forecast revision and its
first lag (as defined here), suggesting considerable sluggishness in forecasts.
In the remaining columns of the table, the variables are changed to correspond to
different horizons, with a focus on revisions made during the current year. In each case, the
estimated coefficient on the lagged revision points to the presence of informational rigidities.
10
IV. INFORMATION RIGIDITY IN RECESSIONS AND CRISES
a. Descriptive Evidence
Figure 1 shows that distributions of actual and forecasted real GDP growth at three
different horizons, April of the previous year, April of the current year and October of the
current year. The distributions for advanced economies are shown in the left panels. The
April year-ahead forecasts are tilted to the right; there are no forecasts of recessions that are
made that far in advance. Current-year forecasts for April start to show some forecasts of
recessions but the number is vastly underestimated. By October, however, the forecasts tend
to converge to the distribution of actual values.
The right-hand panels of Figure 1 provide analogous evidence for emerging market
economies. The April year-ahead forecasts are again tilted to the right, though compared with
the AE group, there are already a few forecasts of recessions. By April of the current-year,
the forecasts start to mirror the actual distribution much better than was the case with the AE
group. By October, the correspondence between the actual and forecasted distributions is
quite good. Overall, the suggestion from this graphical evidence is that a revision of
forecasts, particularly recognition of the possibility recessions, appears to be somewhat faster
for the EM than for the AE group.
Figure 2 provides a more detailed look at the time profile of forecasts in recession and
non-recession years. Each panel provides three pieces of the information: the solid line shows
actual growth in recession years, the dashed line is the unconditional forecast (i.e., the
average forecast for all years) and the bars show the evolution of forecasts in recession years.
Unlike Figure 1, which provided snapshots at different points, Figure 2 shown the evolution
of forecasts over the entire horizon, starting at January of the previous year and ending at
December of the current year?
Consider the evidence for recessions, shown in the top panels of the figure. For the
AE group, the forecasts in recession years start out very close to the unconditional averages.
They start to depart from it slightly around the middle of the previous year, suggesting that
11
forecasters are starting to be aware that the year to come is likely to be a departure from the
norm. Major departures of the forecasts from the unconditional average, however, only start
to occur over the course of the current year and occur in a very smooth fashion. By the end of
the forecasting horizon in December, forecasts are only slightly above the outcome. For the
EM group the deviation of the recession year forecast from the unconditional average
appears from the very start of the forecasting horizon and continues over the course of the
previous year. The biggest revision in forecasts however occurs at the start of the current
year. Revisions continue over the course of the year but the terminal forecast nevertheless
underestimates the decline in real GDP.
The evidence for recessions associated with banking crises is shown in Figure 3. For
advanced economies, the departure from the unconditional forecast starts earlier than it does
for other recessions. This is followed by a smooth pattern of revisions as in the case of all
recession years. However, one difference is that even the terminal forecast vastly
underestimates the actual decline. For the EM group, the recognition starts a bit later than for
the AE group but the extent of the decline is more accurately forecast.
b. Statistical Tests
In Table 6, the regressions reported in Table 5 are augmented by (i) a dummy variable
for recession episodes; and (ii) the interaction between the recession dummy variable and the
lagged forecast revision. The signs of the coefficient estimates on the dummy variable are
negative and significant; not surprisingly, forecast revisions tend to be larger in recession
years than in other years. The interaction terms are also negative, indicating that information
acquisition speeds up during a recession. For both AE and EM groups we cannot reject the
hypothesis that the sum of coefficients on the revision and the interaction is zero. That is, for
recession years, we cannot reject the null of full information rational expectations.
Table 7 presents a similar set of results for recessions associated with banking crises.
Once again, we find that information acquisition speeds up during such recessions. However,
as the results for the two country groups indicate, this result is driven by the emerging
markets group. For this group we cannot reject the null of full information rational
12
expectations. For the AE group, the results are weaker: the coefficient on the interaction term
is negative but not significant in one case and actually positive in the other. We suspect that
this result reflects the small number of episodes of banking crises in our AE sample rather
than any systematic differences in information acquisition across the two country groups
during banking crises.
V. CROSS-COUNTRY LINKAGES IN FORECAST REVISIONS
a. Statistical Framework
In this section, we examine cross-country linkages in forecast revisions using the
framework developed by Isiklar, Lahiri and Loungani (2006). We again exploit the fact that
we have a sequence of revised forecasts of the same event to shed light on how quickly
forecasters absorb new information into their forecasts and how responsive they are to news
from other countries. In the previous section, we considered regressions on forecast revisions
on lagged forecast revisions for the same country to gauge the speed with which information
is absorbed. By the same logic, studying the correlations between the forecast revisions for
one country and the forecasts revisions for other countries tells us to what extent, and how
speedily, news from other countries is absorbed into a country‘s forecasts.
Under the null of full information rational expectations, forecast revisions will reflect
all new information:
, , , 1
( | ) ( | )
t h t t h t t h
r E y E y
(3)
where
,
( | )
t t h
Ey
is the forecast of growth made at horizon h based on the information set
,th
and
,th
r
represents the forecast revision between horizon h and h+1. Denoting the new
information,
, , 1
( | ) ( | )
t t h t t h
E y E y
, as
,th
, one can think of the forecast revision as the
accumulation of past news components so that
, 0 , 1 , 1 2 , 2 3 , 3t h t h t h t h t h
r
(4)
13
where
s
represents the use in today‘s revision of the new information that has been
available s periods ago (
,,i t h s
). If forecasters are fully efficient, then
0
j
for all j>0
should be satisfied. That is, all the information that becomes available should be reflected
immediately in today‘s revision and no information components should be left over to be
utilized in later revisions. Re-writing equation (1) in autoregressive form,
, 1 , 1 2 , 2 , ,t h t h t h p t h p t h
r c B r B r B r
(5)
under the null of full information rational expectations all the B coefficients should be zero.
In a multi-country context, r
t,h
in the equation above is a (J × 1) vector containing the
forecast revisions of the J countries and B
k
is the (J × J) matrix of coefficients of r
t,h+k
. The
diagonal elements of the matrix tell us how quickly forecasters absorb news from their own
country and the off-diagonal elements how quickly they absorb news from other countries.
Note that equation (2) is in the form of a vector autoregressive model (VAR), where
the variables are the forecast revisions of the 7 countries; hence one can use the standard
output from an estimated VAR to describe the results. In particular, the estimated impulse
responses can be used to trace out the effect of a one standard deviation shock to forecast
revisions for country i on the forecast revisions for country j.
The orthogonalized impulse responses and the associated variance decomposition are
sensitive to the ordering of the countries in the VAR. Because of this, we uses generalized
impulse responses and variance decompositions which are ordering-free. Pesaran and Shin
(1998) proposed the method for an ordering free solution in the VAR analysis, and they show
that
1n
vector of k period ahead generalized impulse response of the effect of a one-
standard deviation shock in the j-th country forecast revision equation is given by
12
()
j jj k j
k M e
(6)
where e
j
is the j-th column of an identity matrix and
,,
( ) { , , 1,2,..., }
t h t h ij
E i j n
. M
k
have been defined before. Note that,
has a sample estimate of
,,
ˆ
ˆˆ
1/
t h t h t h
TH
where
,
ˆ
th
is (7×1) residual vector from the estimated VAR model.
14
To compute the speed with which forecasters absorb news over time, we decompose
the variation in forecast revisions into the part accounted for by current innovations and the
part accounted for by past innovations. Specifically, using equation (2), for country k the
percentage of revision variation due to the immediate use of current information is
00
,0
0
kk
k
k i i k
i
e M M e
e M M e
(7)
where
k
e
is the k-th vector of the identity matrix. The numerator of equation (4) is the i-th
diagonal element of the total forecast error variance at horizon zero and
0
i i i i
i
e M M e
is
the variance of k-th element of r
t,h
. Hence
,0k
gives the percentage of the variation in
revisions accounted for by contemporaneous innovations. Similarly the cumulative
percentage of the variation of the revisions within m- periods is
0
,
0
m
k i i k
i
km
k i i k
i
e M M e
e M M e
. (8)
b. Evidence
To estimate equation (2), we use data on forecast revisions for 7 major economies, the
so-called ‗G-3‘ economies (the United States, Japan, Germany) and the BRICs (Brazil,
Russia, India and China). A VAR is estimated, with the lag length set at 3, using the AIC.
In general, the impulse responses show a significant dependence of forecast revisions
on both own-country and cross-country lagged revisions. To quantify the relative importance
of own-country shocks and cross-country shocks, in Table 8 we present the generalized
forecast error variance decompositions.
8
The contribution of own-shocks ranges from about
8
Notice that, in general, generalized variance decompositions do not add up to 100 percent due to non-zero
covariances between the original country shocks, see Pesaran and Shin (1998). The numbers presented here are
normalized so that the total adds up to 100.
15
50% (Brazil) to close to 95% (U.S., Russia). The off-diagonal terms shows the considerable
dependence of Japan and Germany forecast revisions on U.S. revisions, and also the
importance of China—there is substantial dependence of revisions in Japan, Germany and
India on Chinese revisions.
The full set of estimated generalized impulse responses with 2 standard error bands
are given in Appendix III (Figures 1A-G). In the main text we focus on some on some of the
key results from the impulse responses. First, consider the impulse responses to own-country
shocks, shown in Figure 4. Consistent with the evidence from the previous section, in all
seven cases there is sluggishness in the absorption of information. The number of months it
takes to absorb information fully ranges from about 4-5 months (US, Brazil) to about 10
months (Germany, China).
Next, consider the ‗off-diagonal‘ elements—the panels that show the responses of the
forecast revision of one country to the forecast revisions in other countries. First, countries
where there is sluggishness in absorption of own-country information also tend to be sluggish
in absorbing foreign information. Second, most countries show sluggish responses to news
emanating from the US and China, implying that departures from full information rational
expectations arise partly from sufficient attention to news from these countries. As an
illustration, Figure 5 shows the impulse responses of four of the countries to Chinese news.
In Figure 6 we show the speed of absorption of news. As shown, there is quite a bit of
variation across countries in the immediate absorption of news, ranging from 50% to 90%.
However, catch-up is fairly rapid so that by 6 months, in all countries 90% of the news has
been absorbed into forecasts.
VI. CONCLUSIONS
This paper has documented information rigidity in growth forecasts. A novel feature
of our work is that it includes not just forecasts for advanced economies but for all the major
emerging markets. In all we use forecasts for 46 economies over the period 1989 to 2008.
16
Using a test suggested by Coibion and Gorodnichenko (2010), we find that the null of
full information rational expectations is rejected in favor of models with sluggish
incorporation of information. We also exploit the unique structure of our data set—we have
repeated updates of forecasts of a fixed event (viz. annual real GDP growth—to corroborate
the finding of information rigidity using a test based on departures of forecast revisions from
a martingale process.
Putting together the results from the two tests, the preponderance of evidence points
to 4 to 6 months as being the duration it takes forecasters to update their forecasts to fully
reflect new information. This is broadly consistent with the evidence of previous studies for
advanced economies. We find that patterns of information rigidity are similar across
advanced economies and emerging markets, though there is some evidence of somewhat
faster incorporation of information in advanced economies.
Another important result is that the acquisition of information speeds up during
recessions. Not only is the size of forecast revisions larger in recession years than in others,
but the serial correlation in forecast revisions is much lower in recession years than in other
years. In fact, for both advanced and emerging market groups, we cannot reject the null of
full information rational expectations for recession years. We find a similar speeding up of
information acquisition during banking crises, but here the evidence is stronger for emerging
market economies (for which we have many more episodes of banking crises in our sample)
than for advanced economies. These findings support models with state-dependent
acquisition of information (Gorodnichenko 2008).
For a smaller group of seven systemically important economies (the ‗G3‘—U.S.,
Japan, Germany—and the BRICs) we also look at linkages among forecast revisions in a
multivariate VAR model. In addition to corroborating the findings of information rigidity
from the previous univariate tests, this allows us to present evidence on the speed of
absorption of news from other countries in the forecast of a country‘s own growth. One
finding here is that departures from full information rational expectations occur because of
17
slow absorption of news from the U.S. and China into the forecasts of other countries. In
general, the results point to the continued importance of U.S. growth for the ‗G-3‘ and
growing importance of Chinese growth for many countries. The overall speed with which
news is immediately absorbed into forecasts also differs quite a bit across countries.
However, by 6 months, 90% of information is absorbed into forecasts for all seven countries.
Hence, the evidence from this more detailed look at the forecast formation process supports
the tenor of the results from the univariate tests on the speed of updating of information sets.
One limitation of our work is that it relies on the use of consensus forecasts, viz. the
mean across several individual forecasters. In addition to introducing, potentially, some
aggregation bias, the use of the consensus also throws away rich data on forecast formation at
the individual level and the potential for testing interesting behavior such as herding and
group-think. In a companion paper, we study the individual level forecasts to document
information rigidity, state-dependent acquisition of information, and herding behavior in
growth forecasts.
18
Forecast
errors
Absolute
forecast
errors
Forecast
errors
Absolute
forecast
errors
Forecast
errors
Absolute
forecast
errors
Number of observations
Recession
16,120 16,120 6,824 6,824 9,296 9,296
Mean
Recovery
-0.3 1.7 -0.3 1.0 -0.3 2.2
Standard deviation 2.9 2.3 1.4 1.0 3.6 2.8
Number of observations
Recession
2,978 2,978 1,689 1,689 1,289 1,289
Mean
Recovery
-2.7 3.1 -1.4 1.5 -4.5 5.1
Standard deviation 3.8 3.5 1.5 1.5 5.0 4.3
Number of observations
Recession
387 387 117 117 270 270
Mean
Recovery
-4.6 5.0 -2.6 2.6 -5.5 6.1
Standard deviation 5.2 4.8 1.8 1.8 5.9 5.3
Advanced Economies
Emerging economies
Advanced Economies
Emerging economies
All Countries
Recessions following banking crises
All Countries
Recessions
Unconditional
Advanced Economies
Emerging economies
All Countries
Coefficient Coefficient Coefficient
Recessions 2.09 0.07 *** 0.83 0.04 *** 2.95 0.10 ***
Horizon 0.09 0.00 *** 0.05 0.00 *** 0.12 0.00 ***
Constant 0.28 0.03 *** 0.21 0.02 *** 0.32 0.04 ***
Observations 16,120 6,824 9,296
R-squared 0.18 0.20 0.23
Recessions following banking crises 3.40 0.23 *** 1.54 0.15 *** 4.03 0.29 ***
Horizon 0.09 0.00 *** 0.05 0.00 *** 0.11 0.00 ***
Constant 0.53 0.03 *** 0.31 0.02 *** 0.69 0.04 ***
Observations 16,120 6,824 9,296
R-squared 0.12 0.16 0.14
All Countries
Advanced Economies
Emerging
economies
Standard errors
Standard errors
Standard errors
Table 1. Descriptive Statistics
Table 2. Absolute Forecast Errors
19
Coeff. St. Error Coeff. St. Error Coeff. St. Error Coeff. St. Error Coeff. St. Error
Lag 1 0.886 0.308 *** 0.548 0.208 *** 0.362 0.126 *** 0.324 0.069 *** 0.233 0.043 ***
Constant -0.375 0.115 *** 0.349 0.082 *** 0.260 0.063 *** 0.265 0.044 *** 0.232 0.035 ***
Number of observations 757 716 741 757 755
R-squared 0.048 0.109 0.078 0.119 0.092
Lag 1 1.443 0.272 *** 0.468 0.088 *** 0.309 0.086 *** 0.159 0.064 ** -0.005 0.062
Constant -0.279 0.085 *** 0.143 0.063 ** 0.043 0.049 0.051 0.039 0.060 0.034 *
Number of observations 281 267 280 281 282
R-squared 0.138 0.099 0.061 0.026 0.000
Lag 1 0.827 0.334 ** 0.557 0.230 ** 0.372 0.137 *** 0.340 0.075 *** 0.254 0.047 ***
Constant -0.391 0.175 ** 0.461 0.115 *** 0.391 0.096 *** 0.388 0.065 *** 0.327 0.052 ***
Number of observations 476 449 461 476 473
R-squared 0.041 0.111 0.082 0.135 0.114
Dependent variable ¹
Lag 1 ¹
Actual-Sep. py
Actual-Mar. cy
Actual-Sep. cy
Actual-Jun. cy
Actual-Dec. cy
All Countries
Advanced Economies
Emerging Economies
Sep. py-Mar.py
Mar. cy-Sep. py
Sep. cy-Mar. cy
Jun. cy-Dec. py
Dec. cy-Jun. cy
Table 3. Informational Rigidities: Tests Based on Forecast Errors
(6-month horizon)
(
Notes: ¹ cy refers to current year, and py refers to previous year.
20
Coeff. St. Error Coeff. St. Error Coeff. St. Error Coeff. St. Error Coeff. St. Error
Lag 1 0.9091 0.4681 * 0.653 0.378 * 1.197 0.212 *** 0.520 0.115 *** 0.563 0.077 ***
Constant -0.5637 0.1244 *** 0.182 0.078 ** 0.211 0.063 *** 0.192 0.048 *** 0.221 0.034 ***
Number of observations 755 743 757 755 755
R-squared 0.022 0.079 0.164 0.114 0.137
Lag 1 2.2697 0.4092 *** 0.896 0.152 *** 0.511 0.128 *** 0.334 0.108 *** 0.061 0.111
Constant -0.2747 0.0841 *** 0.077 0.057 0.022 0.048 0.048 0.039 0.062 0.033 *
Number of observations 282 280 281 282 282
R-squared 0.150 0.120 0.064 0.039 0.002
Lag 1 0.7638 0.4954 0.641 0.396 1.317 0.249 *** 0.532 0.123 *** 0.619 0.084 ***
Constant -0.6677 0.1898 *** 0.257 0.118 ** 0.320 0.096 *** 0.274 0.073 *** 0.311 0.051 ***
Number of observations 473 463 476 473 473
R-squared 0.015 0.077 0.183 0.123 0.169
Dependent variable ¹
Lag 1 ¹
Sep. py-Jun. py
Mar. cy-Dec. py
Jun. cy-Mar. cy
Sep. cy-Jun. cy
Dec. cy-Sep. cy
All Countries
Advanced Economies
Emerging Economies
Actual-sep py
Actual-Mar. cy
Actual-Jun. cy
Actual-Sep. cy
Actual-Dec. cy
Table 4. Informational Rigidities: Tests Based on Forecast Errors
(3-month horizon)
Notes: ¹ cy refers to current year, and py refers to previous year.
21
Coeff. St. Error Coeff. St. Error Coeff. St. Error Coeff. St. Error
Lag 1 0.528 0.163 *** 0.278 0.054 *** 0.342 0.056 *** 0.372 0.093 ***
Lag 2 -0.181 0.110 0.178 0.082 **
Constant 0.071 0.045 -0.010 0.020 -0.019 0.021 0.038 0.043
Number of observations 711 750 755 741
R-squared 0.233 0.247 0.220 0.165
Lag 1 0.484 0.059 *** 0.372 0.060 *** 0.395 0.056 *** 0.379 0.060 ***
Lag 2 -0.294 0.104 *** 0.051 0.063
Constant 0.050 0.036 -0.009 0.019 -0.010 0.019 -0.014 0.034
Number of observations 266 281 282 280
R-squared 0.195 0.199 0.197 0.158
Lag 1 0.534 0.181 *** 0.266 0.058 *** 0.339 0.060 *** 0.372 0.101 ***
Lag 2 -0.170 0.119 0.204 0.097 **
Constant 0.071 0.062 -0.010 0.031 -0.023 0.033 0.071 0.065
Number of observations 445 469 473 461
R-squared 0.238 0.255 0.223 0.167
Dependent variable ¹
Lag 1 ¹
Lag 2 ¹
Mar. cy-Sep. py
Sep. py-Mar.py
Dec. cy-Sep. cy
All Countries
Advanced Economies
Emerging Economies
Sep. cy-Mar. cy
Sep. cy-Jun. cy
Jun. cy-Mar. cy
Dec. cy-Sep. cy
Sep. cy-Jun. cy
Dec. cy-Jun. cy
Jun. cy-Dec. py
Table 5. Informational Rigidities in Forecast Revisions
Notes: ¹ cy refers to current year, and py refers to previous year.
22
Coeff. St. Error Coeff. St. Error
Lag 1 0.382 0.052 *** 0.445 0.090 ***
Recessions -0.858 0.137 *** -2.175 0.268 ***
Lag 1 * recessions -0.310 0.097 *** -0.458 0.117 ***
Constant 0.068 0.018 *** 0.261 0.038 ***
Number of observations 755 741
R-squared 0.336 0.358
P-values for Wald tests
Lag 1 * recessions 0.371 0.899
Lag 1 0.339 0.063 *** 0.273 0.067 ***
Recessions -0.491 0.077 *** -0.899 0.127 ***
Lag 1 * recessions -0.304 0.184 -0.177 0.227
Constant 0.046 0.019 ** 0.088 0.036 **
Number of observations 282 280
R-squared 0.306 0.275
P-values for Wald tests
Lag 1 * recessions 0.831 0.637
Lag 1 0.390 0.059 *** 0.485 0.111 ***
Recessions -1.118 0.216 *** -2.999 0.386 ***
Lag 1 * recessions -0.375 0.107 *** -0.595 0.140 ***
Constant 0.076 0.026 *** 0.347 0.056 ***
Number of observations 473 461
R-squared 0.359 0.418
P-values for Wald tests
Lag 1 * recessions 0.864 0.317
Dependent variable ¹
Lag 1 ¹
Emerging Economies
Dec. cy-Sep. cy
Sep. cy-Jun. cy
Dec. cy-Jun. cy
Jun. cy-Dec. py
All Countries
Advanced Economies
Table 6. Informational Rigidities during Recessions
Notes: ¹ cy refers to current year, and py refers to previous year.
23
Coeff. St. Error Coeff. St. Error
Lag 1 0.410 0.042 *** 0.466 0.144 ***
Recessions following banking crises -0.997 0.437 ** -1.348 0.513 ***
Lag 1 * recessions following banking crises -0.568 0.113 *** -0.385 0.170 **
Constant -0.014 0.020 0.049 0.042
Number of observations 755 741
R-squared 0.284 0.192
P-values for Wald tests
Lag 1 * recessions following banking crises 0.130 0.378
Lag 1 0.382 0.057 *** 0.354 0.057 ***
Recessions following banking crises -0.612 0.072 *** -0.325 0.387
Lag 1 * recessions following banking crises -0.218 0.065 *** 0.760 0.276 ***
Constant -0.004 0.019 -0.005 0.034
Number of observations 282 280
R-squared 0.224 0.190
P-values for Wald tests
Lag 1 * recessions following banking crises 0.00 0.00
Lag 1 0.413 0.046 *** 0.482 0.169 ***
Recessions following banking crises -1.096 0.606 * -1.492 0.794 *
Lag 1 * recessions following banking crises -0.593 0.133 *** -0.415 0.198 **
Constant -0.020 0.030 0.077 0.065
Number of observations 473 461
R-squared 0.292 0.195
P-values for Wald tests
Lag 1 * recessions following banking crises 0.150 0.539
Dependent variable ¹
Lag 1 ¹
Emerging Economies
Dec. cy-Jun. cy
Jun. cy-Dec. py
Dec. cy-Sep. cy
Sep. cy-Jun. cy
All Countries
Advanced Economies
Table 7. Informational Rigidities during Banking Crises
Notes: ¹ cy refers to current year, and py refers to previous year.
24
USA JAPAN GERMANY BRAZIL RUSSIA INDIA CHINA
USA 81% 3% 10% 1% 1% 2% 2%
JAPAN 10% 54% 3% 3% 16% 3% 10%
GERMANY 15% 3% 64% 11% 0% 1% 6%
BRAZIL 3% 1% 3% 50% 39% 0% 5%
RUSSIA 1% 1% 0% 0% 93% 1% 2%
INDIA 4% 2% 2% 3% 3% 80% 7%
CHINA 3% 4% 1% 8% 1% 3% 79%
Explained by
Table 8. Variance Decompositions
25
Figure 1. Distributions of Actual and Forecasted Real GDP Growth, 1989–2008
Source: Authors' estimates.
0
30
60
90
120
-0.06
-0.02
0.02
0.06
0.10
April of Previous Year
0
30
60
90
120
-0.06
-0.02
0.02
0.06
0.10
April of Current Year
0
30
60
90
120
-0.06
-0.02
0.02
0.06
0.10
October of Current Year
Actual
Forecast
0
30
60
90
120
-0.06
-0.02
0.02
0.06
0.10
April of Previous Year
0
30
60
90
120
-0.06
-0.02
0.02
0.06
0.10
April of Current Year
0
30
60
90
120
-0.06
-0.02
0.02
0.06
0.10
October of Current Year
Advanced Economies
Emerging and Developing Economies
26
-5.0
-3.0
-1.0
1.0
3.0
5.0
7.0
Jan. [-1]
Apr [-1]
Jul [-1]
Oct [-1]
Jan.
Apr
Jul
Oct
Recessions: Advanced Countries
Forecast
Actual
Unconditional forecast
-5.0
-3.0
-1.0
1.0
3.0
5.0
7.0
Jan. [-1]
Apr [-1]
Jul [-1]
Oct [-1]
Jan.
Apr
Jul
Oct
Recessions: Emerging and Developing Countries
Forecast
Actual
Unconditional forecast
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
Jan. [-1]
Apr [-1]
Jul [-1]
Oct [-1]
Jan.
Apr
Jul
Oct
Recessions after Banking Crises: Advanced
Countries
Forecast
Actual
Unconditional forecast
-7.0
-5.0
-3.0
-1.0
1.0
3.0
5.0
7.0
Jan. [-1]
Apr [-1]
Jul [-1]
Oct [-1]
Jan.
Apr
Jul
Oct
Recessions after Banking Crises: Emerging and
Developing Countries
Forecast
Actual
Unconditional forecast
Figure 2. Actual and Forecasted Real GDP Growth during Recessions
Figure 3. Actual and Forecasted Real GDP Growth during Banking Crises
27
Note: The Figure shows confidence intervals for 2 standard deviations.
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of US to US
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Japan to Japan
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Germany to Germany
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Brazil to Brazil
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of India to India
-0.20
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Russia to Russia
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of China to China
Figure 4. Generalized Impulse Responses of Forecast Revisions
(Own-country responses; in percentage points)
28
Note: The Figure shows confidence intervals for 2 standard deviations.
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of US to China
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Japan to China
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Germany to China
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of India to China
Figure 5. Generalized Impulse Responses of Forecast Revisions
(Response to Chinese revisions; in percentage points)
29
50%
60%
70%
80%
90%
100%
110%
0
1
2
3
4
5
6
7
8
9
10
11
12
Variance Decomposition
USA
JAPAN
GERMANY
BRAZIL
RUSSIA
INDIA
CHINA
Figure 6. Speed of Absorption of News
30
APPENDIX I. DESCRIPTION OF SAMPLE
Country name Region Starting date Banking crisis Recession
AUSTRALIA* Asia Jan-90
1990Q2-91Q2
CANADA* Western Hemisphere Oct-89
1990Q2-91Q1, 2008Q4-09Q2
FRANCE* Europe Oct-89
1992Q2-93Q3, 2002Q4-03Q2, 2008Q2-09Q1
GERMANY* Europe Oct-89
1992Q2-93Q1, 1995Q4-96Q1, 2002Q4-04Q3,
2008Q2-09Q1
GREECE* Europe Jun-93
1990Q2-90Q3,1992Q2-93Q1,1994Q4-95Q2,
2008Q4-09Q3
ITALY* Europe Oct-89
1992Q2-93Q3, 1996Q2-96Q4, 2001Q2-01Q4,
2003Q1-03Q2, 2004Q4-05Q1, 2008Q2-09Q2
JAPAN* Asia Oct-89 1997
1993Q2-93Q4, 1997Q2-99Q1, 2001Q2-01Q4,
2008Q2-09Q1
NETHERLANDS* Europe Nov-89 2008
2008Q2-09Q2
NEW ZEALAND* Asia Nov-89
1991Q1-91Q2, 1997Q4-98Q1, 2008Q1-09Q1
NORWAY* Europe Nov-89
2002Q3-03Q1, 2008Q3-09Q2
SPAIN* Europe Nov-89
1992Q2-93Q2, 2008Q2-09Q3
SWEDEN* Europe Nov-89 1991
1990Q2-93Q1, 2008Q2-09Q1
SWITZERLAND* Europe Nov-89
1990Q3-93Q1, 1996Q2-96Q3, 1998Q4-99Q1,
2001Q2-03Q1, 2008Q3-09Q2
UNITED STATES* Western Hemisphere Oct-89 2007, 2008
1990Q4-91Q1, 2001Q1-01Q3, 2008Q1-09Q2
UNITED KINGDOM* Europe Oct-89 2007, 2008
1990Q3-91Q3, 2008Q2-09Q3
Number of countries or episodes 7 45
Appendix Table 1. List of Countries, Recessions and Crisis Episodes
Advanced economies
Sources: International Financial Statistics; Claessens, Kose and Terrones (2008); Laeven and Valencia (2008).
Notes: The classification of countries into advanced, emerging and developing is aligned with Consensus Forecasts publications.
Countries for which the dating of recession and recovery episodes is based on quarterly data are marked with an asterisk. Only
crises during the time period for which consensus forecasts are available are reported.
31
Country name Starting date Banking crisis Recession
ARGENTINA* Western Hemisphere Mar-93 1995, 2001 1995Q2-96Q1, 1998Q4-02Q4
BRAZIL* Western Hemisphere Nov-89 1990, 1994
1990Q2-91Q1, 1992Q2-92Q4, 1995Q4-96Q1,
1998Q4-99Q3, 2001Q4-02Q1, 2008Q4-09Q1
BULGARIA Europe Jan-95 1996 1996-97, 2008Q4-09Q1
CHILE* Western Hemisphere Mar-93 1998Q4-99Q3, 2008Q3-09Q2
CHINA* Asia Dec-94 1998
COLOMBIA* Western Hemisphere Mar-93 1998 1998Q3-99Q4, 2008Q3-08Q4
CROATIA* Europe May-98 1998 1998Q3-99Q4, 2008Q2-09Q2
CZECH REPUBLIC* Europe Jan-95 1996 1997Q3-98Q4, 2008Q4-09Q1
ESTONIA* Europe May-98 1999Q1-99Q3, 2008Q1-09Q3
HONG KONG* Asia Nov-90 1997Q4-98Q4, 2001Q1-03Q4, 2008Q2-09Q1
HUNGARY* Europe Nov-90 1991 1990-93, 2008Q2-09Q2
INDIA* Asia Dec-94 1993
INDONESIA* Asia Nov-90 1997 1998Q1-99Q1
LATVIA* Europe May-98 1993Q1-94Q1, 1996Q4-97Q1, 2008Q4-09Q2
LITHUANIA* Europe May-98 1999Q2-99Q4, 2008Q3-09Q2
MALAYSIA* Asia Nov-90 1997 1998Q1-99Q1, 2008Q4-09Q1
MEXICO* Western Hemisphere Nov-89 1994 1995Q1-95Q4, 2001Q3-02Q1, 2008Q2-09Q2
PERU* Western Hemisphere Mar-93 1998Q2-99Q3, 2000Q4-01Q2, 2008Q4-09Q1
PHILIPPINES* Asia Dec-94 1997 1998Q2-98Q4
POLAND* Europe Nov-90 1992 1990Q1-92Q1, 2008Q4-09Q1
ROMANIA Europe Jan-95 1997-1998, 2008Q3-09Q3
REPUBLIC OF KOREA* Asia Nov-89 1997 1998Q1-98Q4
SINGAPORE* Asia Nov-90 2001, 2008
SLOVAK REPUBLIC* Europe Jan-95 1998 1999Q3-00Q1, 2008Q4-09Q1
SLOVENIA* Europe Jan-95
SOUTH AFRICA* Africa Jun-93 2008Q4-09Q2
TAIWAN* Asia Nov-89 2001Q2-01Q4, 2008Q2-09Q1
THAILAND* Asia Nov-90 1997 1997Q2-99Q1, 2008Q2-09Q1
TURKEY* Europe Jan-95 2000 1999Q1-99Q4, 2001Q1-02Q1, 2008Q2-09Q1
UKRAINE Europe Jan-95 1998 2008Q3-09Q1
VENEZUELA* Western Hemisphere Mar-93 1994
1993Q1-94Q4, 1996Q2-96Q3, 1998Q3-99Q4,
Number of countries or episodes 22 61
Sources: International Financial Statistics; Claessens, Kose and Terrones (2008); Laeven and Valencia (2008).
Notes: The classification of countries into advanced, emerging and developing is aligned with Consensus Forecasts publications.
Countries for which the dating of recession and recovery episodes is based on quarterly data are marked with an asterisk. Only
crises during the time period for which consensus forecasts are available are reported.
Appendix Table 1. List of Countries, Recessions and Crisis Episodes, continued
Emerging economies
32
APPENDIX II. FREQUENCY OF DATA
Country
Start Date of Bi-
monthly Data
Start Date of
Monthly Data
Second Start of Monthly Data If Data Frequency Was
Changed From Monthly to Bi-monthly to Monthly
ARGENTINA 1993m3 2001m8 .
AUSTRALIA . 1990m1 .
BRAZIL 1993m6 1989m11 2001m8
BULGARIA 1998m6 1995m1 2007m5
CANADA . 1989m10 .
CHILE 1993m3 2001m8 .
CHINA . 1994m12 .
COLOMBIA 1993m3 2001m8 .
CROATIA 1998m5 2007m5 .
CZECH REPUBLIC 1998m6 1995m1 2007m5
ESTONIA 1998m5 2007m5 .
FRANCE . 1989m10 .
GERMANY . 1989m10 .
GREECE . 1993m6 .
HONG KONG . 1990m11 .
HUNGARY 1998m6 1990m11 2007m5
INDIA . 1994m12 .
INDONESIA . 1990m11 .
ITALY . 1989m10 .
JAPAN . 1989m10 .
LATVIA 1998m5 2007m5 .
LITHUANIA 1998m5 2007m5 .
MALAYSIA . 1990m11 .
MEXICO 1993m6 1989m11 2001m8
NETHERLANDS . 1989m11 .
NEW ZEALAND . 1989m11 .
NORWAY . 1989m11 .
PERU 1993m3 2001m8 .
PHILIPPINES . 1994m12 .
POLAND 1998m6 1990m11 2007m5
ROMANIA 1998m6 1995m1 2007m5
RUSSIA . 1995m1 .
SINGAPORE 1998m6 1995m1 2007m5
SLOVAKIA 1998m6 1995m1 2007m5
SLOVENIA . 1993m6 .
SOUTH AFRICA . 1989m11 .
SPAIN . 1989m11 .
SWEDEN . 1989m11 .
SWITZERLAND . 1989m11 .
TAIWAN . 1989m11 .
THAILAND . 1990m11 .
TURKEY 1998m6 1995m1 2007m5
U.S.A. . 1989m10 .
UKRAINE 1998m6 1995m1 2007m5
UNITED KINGDOM . 1989m10 .
VENEZUELA 1993m3 2001m8 .
33
APPENDIX III. Generalized Impulse Responses of Forecast Revisions
Note: The Figure shows confidence intervals for 2 standard deviations.
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of US to US
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of US to Japan
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of US to Germany
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of US to Brazil
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of US to Russia
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of US to India
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of US to China
Figure 1A. Generalized Impulse Responses of U.S. Forecast Revisions
(In percentage points)
34
-0.03
-0.01
0.01
0.03
0.05
0.07
0.09
0.11
0.13
0.15
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Germany to US
-0.03
-0.01
0.01
0.03
0.05
0.07
0.09
0.11
0.13
0.15
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Germany to Japan
-0.03
-0.01
0.01
0.03
0.05
0.07
0.09
0.11
0.13
0.15
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Germany to Germany
-0.03
-0.01
0.01
0.03
0.05
0.07
0.09
0.11
0.13
0.15
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Germany to Brazil
-0.03
-0.01
0.01
0.03
0.05
0.07
0.09
0.11
0.13
0.15
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Germany to Russia
-0.03
-0.01
0.01
0.03
0.05
0.07
0.09
0.11
0.13
0.15
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Germany to India
-0.03
-0.01
0.01
0.03
0.05
0.07
0.09
0.11
0.13
0.15
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Germany to China
Note: The Figure shows confidence intervals for 2 standard deviations.
Figure 1B. Generalized Impulse Responses of Germany's Forecast
Revisions
(In percentage points)
35
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Japan to US
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Japan to Japan
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Japan to Germany
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Japan to Brazil
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Japan to Russia
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Japan to India
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Japan to China
Note: The Figure shows confidence intervals for 2 standard deviations.
Figure 1C. Generalized Impulse Responses of Japan's Forecast Revisions
(In percentage points)
36
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Brazil to US
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Brazil to Japan
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Brazil to Germany
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Brazil to Brazil
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Brazil to Russia
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Brazil to India
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Brazil to China
Note: The Figure shows confidence intervals for 2 standard deviations.
Figure 1D. Generalized Impulse Responses of Brazil's Forecast Revisions
(In percentage points)
37
-0.04
0.01
0.06
0.11
0.16
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of China to US
-0.04
0.01
0.06
0.11
0.16
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of China to Japan
-0.04
0.01
0.06
0.11
0.16
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of China to Germany
-0.04
0.01
0.06
0.11
0.16
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of China to Brazil
-0.04
0.01
0.06
0.11
0.16
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of China to Russia
-0.04
0.01
0.06
0.11
0.16
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of China to India
-0.04
0.01
0.06
0.11
0.16
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of China to China
Note: The Figure shows confidence intervals for 2 standard deviations.
Figure 1E. Generalized Impulse Responses of China's Forecast Revisions
(In percentage points)
38
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of India to US
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of India to Japan
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of India to Germany
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of India to Brazil
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of India to Russia
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of India to India
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of India to China
Note: The Figure shows confidence intervals for 2 standard deviations.
Figure 1F. Generalized Impulse Responses of India's Forecast Revisions
(In percentage points)
39
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Russia to US
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Russia to Japan
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Russia to Germany
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Russia to Brazil
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Russia to Russia
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Russia to India
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Response of Russia to China
Note: The Figure shows confidence intervals for 2 standard deviations.
Figure 1G. Generalized Impulse Responses of Russia's Forecast Revisions
(In percentage points)
40
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