Download 1. Overview

Statistics, Knowledge and Policy
OECD World Forum on Key Indicators
Palermo, 10-13 November 2004
THE USE AND ABUSE OF REAL-TIME AND ANECDOTAL
INFORMATION IN MONETARY POLICYMAKING*
EVAN F. KOENIG
Vice President and Senior Economist
Federal Reserve Bank of Dallas
* The views expressed are those of the author, and may not reflect the views of the Federal Reserve Bank of Dallas or the Federal
Reserve System.
1.
Overview
The main message of this paper is that policymakers should not necessarily take official
government statistics at face value and should be open to alternative sources of information, including
anecdotal reports and surveys. Specific instances are cited where official statistics have been misleading
and where anecdotal or survey information has provided early warning of important changes in the
economy.
Policymakers know not to put their faith in data series that are subject to large revisions.
However, they often fail to recognize the extent to which the forecasts and policy advice they receive
come from models that are estimated and evaluated ignoring revisions, and whose performance, therefore,
is at once sub-optimal and overstated. In much the same vein, policy rules that seem to perform well in
after-the-fact evaluations often perform poorly in real time.
Recent research has shed light on how forecasting relationships are properly estimated when
data are subject to revision and has demonstrated that the payoff to correct estimation is often substantial.
http://www.oecd.org/oecdworldforum
Archival requirements are not as onerous as might be expected, and historical vintage data sets have
become more readily available.
The extent to which anecdotal reports and qualitative surveys are useful supplements to official
statistical releases is underappreciated. Such reports and surveys are often more timely than official
statistics. Moreover, respondents seem to filter out some of the short-term noise that makes economic
turning points difficult to recognize in real time. Of course, skill and care are required when interpreting
qualitative information just as much as when interpreting quantitative data. It helps if the qualitative
information is collected from a large number and variety of sources and if continuity of sources is
maintained over an extended period. A geographically decentralized institutional structure, like that of
the Federal Reserve, facilitates the flow of anecdotal information to policymakers.
Financial asset markets are forward-looking. Financial asset prices are available almost
continuously and are not subject to revision. Consequently, these prices might seem to be ideal
forecasting tools. Unfortunately, however, the links between asset prices and the real economy are not
always straightforward, and the policy expectations implicit in asset prices are sometimes unrealistic.
2. The Importance of Revisions: An Example
The mid 1990s–from 1991 through 1998–were a period when economic forecasters overpredicted U.S. inflation year after year despite generally stronger-than-expected real GDP growth (Figure
1).1 The unemployment rate consistent with stable inflation–the “non-accelerating inflation rate of
unemployment” or “NAIRU”–was thought to exceed 6 percent at the beginning of the period, but NAIRU
estimates were gradually revised downward as evidence that the economy could sustain a lower rate of
unemployment accumulated (Gordon 1993, 1997, 2000, 2003). Today’s estimates show a NAIRU path
that declines from 6.2 to 5.0 percent over the decade of the 1990s, and which is below real-time estimates
by anywhere from 0.2 to 0.4 percentage points. Revisions of this magnitude can be important. As of
September, the U.S. unemployment rate was 5.4 percent. Consequently, if the current 5.0-percent
NAIRU estimate understates the true value by 0.4 percentage points, then there is no slack remaining in
the U.S. labor market. On the other hand, if the current estimate overstates the true value by 0.4
percentage points, we have room for quite rapid growth in coming quarters without triggering upward
inflation pressure. Revisions in the estimated NAIRU appear to translate into immediate, 1-for-1
movements in the Federal Reserve’s target short-term interest rate (Koenig 2004). Thus, a NAIRU reevaluation of the magnitude we saw in the late 1990s would mean a roughly 50-basis-point immediate
change in the federal funds rate in the same direction.
The importance of obtaining accurate real-time NAIRU estimates is brought home by
Orphanides in a series of influential papers that examine the U.S. “Great Inflation” of the 1970s
(Orphanides 2002, 2003). Orphanides argues that the main problem with U.S. monetary policy during the
Great Inflation period was that it took at face value estimates of economic slack that proved,
subsequently, to be far off base.
Not surprisingly, in view of their importance for economic performance and policy,
considerable effort has been devoted to trying to understand the causes of NAIRU movements. Recent
research suggests that changes in profitability–influenced, in turn, by changes in the trend rate of labor
1
Over this eight-year period, actual Q4 over Q4 GDP growth exceeded the Blue Chip consensus forecast by an
average of 1.4 percentage points.
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productivity growth–are one important source of NAIRU variation (Brayton, Roberts and Williams 1999;
Koenig 2001). Economic theory and common sense tell us that firms will want to expand production and
employment whenever worker productivity (output per hour) is high relative to labor’s real cost (the real
wage rate), and to contract production and employment whenever productivity is low relative to labor’s
cost. When productivity accelerates, wages may lag productivity initially, raising profitability and
encouraging non-inflationary output and employment growth. Arguably, this scenario describes the
1990s. When productivity decelerates, productivity may lag wages for a time, lowering profitability and
creating a 1970s-style tendency toward stagflation. More generally, movements in the NAIRU appear to
be heavily influenced by movements in profitability, as measured by the ratio of labor productivity to the
real wage. Chairman Greenspan has emphasized this connection in Congressional testimony (Greenspan
2004).
Figure 2, adapted from Koenig (2001), presents evidence consistent with the Chairman’s
testimony. It shows movements in the NAIRU during the 1970s, 1980s and 1990s, inferred after the fact
from the behavior of inflation, and also shows the ratio of productivity to the real wage in the non-farm
business sector.2 A strong positive correlation is evident. In particular, the estimated NAIRU rose
markedly around 1970, at roughly the same time that profitability fell. Similarly, the estimated NAIRU
fell markedly in the early 1990s, coincident with a rise in profitability.
Alternatively, Figure 3 shows a strong inverse, leading relationship between profitability and
the realized unemployment rate. The intuition behind the figure is as follows: As policymakers gradually
become aware of profitability-driven changes in the NAIRU, they adjust interest rates to stabilize
inflation and achieve maximum non-inflationary employment, driving the realized unemployment rate
toward the NAIRU. The unemployment rate falls with a lag in response to high profitability (a low
NAIRU), and rises with a lag in response to low profitability (a high NAIRU). The lag between
movements in profitability and movements in the unemployment rate is about three years.
Why the slow policy response? Part of the explanation is that the link between profitability and
the NAIRU has only recently been recognized, so policymakers historically have not monitored
profitability very closely. Another important part of the explanation, however, is displayed in Figure 4,
which shows the ratio of labor productivity to the real wage as it appears in today’s data, and as initially
estimated. Clearly, the profitability data have been subject to large revisions: over the period from
1981:Q4 through 2002:Q4, the correlation between the two series is only 0.22. Post 1990, it drops to
0.08. When today’s data are used, profitability appears to have strong predictive power for inflation, as
one would expect if movements in profitability are closely related to changes in the NAIRU. But when
first-release data are used–the data actually available to policymakers in real time–then profitability’s
predictive power disappears (Koenig 2003). Profitability may, in other words, be quite useful for
explaining shifts in the inflation–unemployment trade-off after the fact, but–at least as captured in the
official statistics–it is of no help in recognizing NAIRU shifts in real time.
In summary, good policy depends on information that is both accurate and timely. Variables
that appear helpful for policy in analyses that rely on data as they appear today may be of little use in real
time. History teaches that big policy mistakes are possible if decision makers take the results of such
analyses at face value.
2
To obtain the estimated NAIRU, the Kalman filter is applied to a Phillips-curve inflation equation for the non-farm
business price deflator. See Koenig (2001) for details.
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3. Different Types of Revisions
Not all official data series are subject to significant ex post revision. In the United States, the
consumer price index (CPI) and the unemployment rate are prominent examples of series whose historical
values are revised only to reflect new estimates of seasonal patterns. These revisions are trivial compared
with the series’ month-to-month variation, and can usually be ignored.
At the other extreme, major conceptual and methodological changes are potentially quite
problematic. Pre-revision and post-revision data may be non-comparable, and due allowance must be
made for possible structural breaks in estimated relationships. Examples of important conceptual and
methodological changes include the recent switch from the Standard Industrial Classification system to
the North American Industry Classification System for purposes of tracking sectoral movements in sales,
production, and employment; the 1983 change in the Labor Department’s treatment of housing prices in
the consumer price index; the Commerce Department’s 1999 decision to reclassify business software
purchases as investment for purposes of calculating GDP; and the Labor Department’s 1994 redefinition
of “discouraged workers.”
More routinely, revisions occur as new, more complete source data become available. The
“advance” U.S. GDP report, for example, is released one month after the close of the quarter, before all
data on investment, international trade and government purchases are available. The Bureau of Economic
Analysis substitutes educated guesses for the missing data. A “preliminary” GDP report is released two
months after the close of the quarter–at which point some of the missing data have been filled in. The socalled “final” report is released a month after that, and an additional revision is released annually. (The
annual revision covers the three prior calendar years plus the quarters already released for the current
year.) Revisions to important monthly series like payroll employment and industrial production follow a
basically similar pattern. An advance estimate for each given month, based on incomplete data, is
released during the following month. This estimate is revised in each of the subsequent two or three
months, as missing data are filled in. There are also annual revisions to each series.
The extent to which movements in early statistical releases for a variable are a reliable guide to
the true movements in that variable depends very much on how the relevant statistical agency fills in for
missing source data (Sargent 1989). Most commonly, it is assumed that the agency simply extrapolates
from the limited data initially available, so that early releases equal the truth plus statistical noise.
According to this “noise” model, early statistical releases are more variable than later releases (which are
based on larger samples). Moreover, if, say, GDP shows an out-size increase in the initial report, one can
properly take the view that the increase is likely partly noise that will subsequently be revised away, as
more complete source data become available. If GDP shows an out-size decline, similarly, then probably
it will be revised upward in the future. Thus, future data revisions are negatively correlated with the
statistical agency’s early estimates. They are uncorrelated with the truth.
The “news” model of early statistical releases, in contrast, assumes that the statistical agency is
sophisticated enough to look not just at currently available source data, but also at the past behavior of the
variable in question and at other indicators as it tries to anticipate late-arriving source data. The agency
announces its best estimates of the truth, given all of the information at its disposal. In this case, the truth
equals the early release plus statistical noise, rather than the other way around. Early releases are less
variable than later releases. Moreover, future data revisions are completely uncorrelated with the
statistical agency’s early estimates. Indeed, they are uncorrelated with any and all information available
to the statistical agency at the time of the early estimates’ release. They are positively correlated with the
truth.
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From the policymaker’s perspective, whether a particular statistical report includes noise or is
pure news is vital, because it determines whether that report ought to be second-guessed or ought to be
accepted as the best estimate currently available. As noted above, an easy first-pass test is to determine
whether early releases are more or less variable than revised releases. If more variable, it’s likely the
early releases can be second-guessed. Alternatively, one can look at the correlation between data
revisions and early releases. Second-guessing is possible if this correlation is non-zero. (The noise
model suggests that it ought to be negative.) Results of these simple tests are displayed in Table 1 for
GDP, payroll employment, and industrial production. In each case, the test results fit the news model
better than they fit the noise model.
4. Forecasting When Data Are Subject to Revision
Policymakers often fail to realize the extent to which the forecasts and policy advice they
receive are based on models that are estimated either ignoring or mishandling data reliability issues. In
particular, forecasting models are nearly always estimated using end-of-sample-vintage data–the latest
data available at the time the estimation is performed. Similarly, when two or more variables are plotted
together with a view towards displaying a lead/lag relationship between them, it is usually end-of-samplevintage data that are shown. (Figure 3, discussed above, is an example.) The first problem is that these
data are a mix of statistics that have been heavily revised and statistics that are only lightly revised–a mix
of apples and oranges–and there is no reason to believe that the apples are related to one another in the
same way as the oranges. In practice, for reasonably sized samples, heavily revised data–the apples–will
dominate coefficient estimates. But this dominance creates a serious second problem when it comes time
to use the model for forecasting, because the data that will be plugged into the estimated equations to
produce a forecast are, inevitably, first release or lightly revised–oranges. Conventional practice
constructs a cider press and then feeds oranges into it, expecting to get cider.
Looking at charts, the eye does much the same thing as a regression. There is a natural
tendency to extrapolate the relationship between heavily revised data and heavily revised data (apples and
apples) that applies to the greatest portion of the chart and assume that it holds for the most recent data,
too, which are first release or lightly revised (oranges). In Figure 3, there’s a tendency to think that
because increases in profitability have, in the past, led declines in the unemployment rate, the most recent
rise in profitability also portends an unemployment-rate decline. The past profitability increases plotted
in the chart, however, incorporate the effects of many rounds of data revisions. The most recent rise in
profitability could easily be revised away as additional source data become available.
The correct way to try to make a case that profitability is useful for predicting unemployment is
to plot the unemployment rate with the early release profitability data that would actually have been
available to a forecaster in real time. More generally, regression equations should relate whatever
quantity is being forecast to the first-release and lightly revised data that would have been available at the
time the forecast was prepared (Koenig, Dolmas and Piger 2003). It’s oranges that belong on the righthand side of forecast-equation regressions.
What vintage data ought to be on the left-hand side of a forecasting equation for estimation
purposes? Presumably we are ultimately interested in forecasting the “truth”–what the left-hand-side
variable will look like after it is thoroughly revised: We want to forecast apples. Even so, surprisingly, it
may be oranges that belong on the left-hand side of the forecast-equation regression. Oranges do better
than apples as a left-hand-side variable whenever the government statistical agency’s early estimates of
that variable are pure “news.” Recall that an early estimate is “news” if it makes full use of the
information that’s available at the time that the estimate is prepared. Revisions to such estimates are
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completely unpredictable. From the perspective of the forecaster, then, the truth is the early estimate plus
unpredictable noise. By using the early estimate on its left-hand side, the analyst eliminates unpredictable
noise from the regression equation. Bottom line: apples are always safe for the left-hand side of the
regression equation (because it’s apples we ultimately wish to predict), but oranges will work better if
oranges are the best-available estimates of the apples (Koenig et. al. 2003).
Based on the results in Table 1, there is little evidence of predictability in GDP, payroll
employment and industrial production revisions. Hence, forecasting equations for these variables
arguably ought to be estimated with early-release data (oranges) on the left-hand side.
In real-world settings, the payoff to proper estimation of forecasting equations can be
substantial. In joint work forecasting current-quarter GDP growth, Jeremy Piger, Sheila Dolmas and I
were able to cut root-mean-square forecast errors by 20 percent using the approach outlined here,
compared with conventional estimation (Koenig et. al. 2003).
5. Data Requirements
As noted above, conventional practice is to estimate forecasting equations using latest-available
data. Each period, as new government statistical estimates are released, the previous period’s data are
thrown out, and replaced with data of the newer vintage. Forecasting equations are re-estimated using the
new data, which extends out one period beyond the old data, and a new forecast is prepared. In common
parlance, a “real-time” forecasting exercise is just an after-the-fact reproduction of this data, estimation,
and forecast updating routine. For example, data from 2000, and extending back 50 years, might be used
to estimate a model and produce a forecast of 2001 GDP growth. Then, data from 2001, extending back
51 years, would be used to re-estimate the model and produce a 2002 forecast. Etc. At each iteration, the
entire data set is updated and used to obtain updated coefficient estimates and a new forecast.
Carrying out this type of real-time recursive forecasting exercise requires a sequence of long
data sets of different vintages. In my example, one would need a 50-year history of year 2000-vintage
data, a 51-year history of 2001-vintage data, and so forth. Long time series of different-vintage data are
difficult to assemble, and the prospect of collecting the required data has proven sufficiently daunting that
real-time forecasting exercises of this type are rare.3 The task has been made much easier recently,
courtesy of Dean Croushore (currently at the University of Richmond) and the Federal Reserve Bank of
Philadelphia, who have posted a collection of different-vintage data on the internet
(www.philadelphiafed.org).
An after-the-fact reproduction of conventional forecasting practice is of only limited interest if
conventional practice is misguided, as was argued above. The correct way to estimate forecasting
equations is to use, at each point within the sample period, only right-hand-side data that would have been
available at that point. As time passes and the sample period is extended, old data are not thrown out and
replaced with data of the latest vintage. Instead, old data are retained and latest-vintage data are added at
the end of the sample. Estimation using this method requires short time series of many different vintages
(one vintage for each date in the sample, regardless of the number of estimations), rather than long time
3
Diebold and Rudebusch (1991) is a well known, early example.
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series of several different vintages (one vintage for each estimation, regardless of the number of dates in
each sample).4
At present, the short series of many vintages required for proper real-time estimation and
forecasting are easily extracted from hard copies of government publications. However, insofar as
statistical agencies move away from print and toward purely electronic publication, after-the-fact
assembly of real-time data sets will become more difficult. A conscious effort will be required to archive
data as it appears at the time of each statistical release.
6. Anecdotal and Qualitative Alternatives to Conventional Statistical Reports
Oftentimes, the first indication of an important shift in the economy comes not from official
government statistics, but from anecdotal reports and other sources of qualitative information. During the
mid 1990s, for example, business executives began talking about a lack of pricing power and a relentless
pressure to cut costs. Only later did surging productivity growth and the resultant high profitability
become apparent in official statistics. Similarly, the first warning of the 2001 high technology downturn
came from business contacts in the 11th Federal Reserve District–home to Texas Instruments and the
Compaq and Dell computer companies.
One vital source of qualitative information relevant to U.S. monetary policy are the directors of
the twelve regional Federal Reserve Banks and their branches. These directors–who include commercial
bankers, business people, and community leaders–have an opportunity to communicate their economic
knowledge and concerns directly to the regional Federal Reserve Bank presidents during bi-weekly
conference calls and monthly District Board meetings. In addition, each regional Reserve Bank surveys a
cross section of area business contacts about recent economic developments in advance of Federal Open
Market Committee policy meetings. Summaries are prepared by the Banks, and the summaries are
assembled into a document called the “Beige Book,” which is available to both policymakers and the
public.
Evidence of the value of the qualitative information obtained from business contacts takes
several forms. There is, first, the attention that the Beige Book receives when it is released to the public.
4
A photo-album analogy may help clarify the differences in data requirements. Conventional procedure is to keep a
single photo album that has, say, one photo per family member for each year since the family’s inception. When a year passes, a
new album is purchased and the old album is thrown away. The latest year’s photos appear at the very back of the new album, on
their own page. Photos from earlier years are mostly duplicates of those that were in the old album. But if Aunt Mildred has sent
a picture of little Jimmy at age 10, and Aunt Mildred’s photo is an improvement over the photo of 10-year-old Jimmy currently in
the album, then the existing photograph is culled in favor of Aunt Mildred’s contribution. Conventional recursive “real-time”
analysis requires that one retain albums from earlier years, instead of throwing them out. Aside from those on the back pages,
photos in successive albums are mostly duplicates. The Philadelphia Fed web site cited above has an album collection of this
sort. For the real-time estimation approach proposed here, one needs to maintain a rather different photo collection. Old
photographs are never thrown out or replaced. However, each year you add two new pages to your album. The first new page
contains photos taken during the year just ended. The second contains photos taken by Aunt Mildred a year earlier, but which
have only just arrived, and which are improvements over existing photos. There is only one album, but it has twice as many
pages as a conventional album. (Just how much thicker the single album is depends on how many lags of each right-hand-side
variable appear in the forecasting equation. In this example, I assume two lags. If three lagged values appear in the forecasting
equation, the single album would have three pages for each one page of the conventional album. Etc.)
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Press coverage is often extensive, especially when there is concern that the economy may be changing
course. Just as one example, the Wall Street Journal often prominently displays a Beige Book analysis on
its second page the day following the report’s release. Other evidence comes from scholarly articles.
Researchers have read through old Beige Books, assigning them numerical scores based on the language
used to describe unfolding developments. These numerical scores have been shown to have statistically
significant predictive power for the national and regional economies–predictive power beyond that of
conventional economic indicators available at the time of the Beige Book’s release (Balke and Yucel
2000; Balke and Petersen 2002).
The Institute for Supply Management (ISM) is an important source of qualitative information
external to the Federal Reserve System. Each month, the institute surveys purchasing managers from
over 400 industrial companies, asking whether orders, production, employment and other important
indicators of business conditions are increasing, the same, or decreasing at their respective firms. 5
Diffusion indexes are constructed from the responses. Each diffusion index measures the percentage of
respondents reporting that a particular indicator is rising, plus one half the percentage of respondents
reporting that the indicator is unchanged. So, any reading above 50 signals that more firms are reporting
increases than are reporting decreases. A large firm is given no greater weight than a small firm, and a
firm experiencing rapid increases (or decreases ) is given no greater weight than a firm experiencing
modest increases (decreases). A summary diffusion index, the Purchasing Managers’ Index (PMI),
provides an overall assessment of whether the manufacturing sector is expanding or contracting.6
Compelling evidence of the information content of the PMI comes from financial markets. A
recent Goldman Sachs study documents that medium-term interest rates respond more strongly to PMI
surprises than to surprises in any other economic report, including the GDP, employment, retail sales, and
industrial production reports.7 The PMI has also been shown to have information for GDP growth
beyond what can be inferred from growth in employment, industrial production, and retail sales (Koenig
2002).
Why are the Federal Reserve’s Beige Book and the ISM’s Purchasing Managers’ Index so
valuable? After all, given the qualitative nature of the surveys and their unscientific sampling, disputes
about interpretation are inevitable. One advantage is timeliness. The PMI for each month is released on
the first business day of the following month and is the earliest available economic indicator with broad
coverage. The Beige Book survey schedule is closely coordinated with that of the Federal Open Market
Committee, which sets short-term interest rates. In contrast, most government statistical reports are
released with a lag of two weeks or more, are based on incomplete source data, and may be as much as a
month out of date by the time that the FOMC gathers. Second, as discussed above, supposedly “hard”
government statistical reports are subject to substantial after-the-fact revision–which means that their
proper interpretation is not necessarily any more straightforward than the interpretation of “soft,”
5
The ISM began a similar survey for the non-manufacturing sector in 1997 (versus 1948 for manufacturing).
6
Six of the regional Federal Reserve Banks now conduct ISM-style surveys and compute ISM-style diffusion
indexes for their districts, as a supplement to their Beige Book reports. The oldest such survey (Philadelphia) goes back to 1968.
7
See Hatzius and Crump (2003). A “surprise” is a one-standard-deviation difference between the realization of an
indicator and the Wall Street consensus forecast for that indicator. The financial-market impact is measured by the change in the
two-year Treasury Note yield.
Page 8 of 15
qualitative survey results.8 Finally, often policymakers are less concerned with the precise “truth” of
what happened to output, employment, or sales in a particular month than they are about emerging trends
in the data. Business executives appear to have a knack for recognizing these trends, and filtering out
transitory fluctuations. For an example, see Figure 5, which plots 3-month annualized growth in
manufacturing output along with a 3-month moving average of the PMI. The PMI picks up the persistent
movements in factory output growth, and eliminates sharp month-to-month swings.
In practice, although anecdotal information may alert Federal Reserve policymakers to
emerging trends and important shifts in the economy, action usually is deferred until there is some harddata confirmation of the new trend or shift. Occasionally, however, waiting a month or more for
government statistical reports to arrive is not an option. Such was the case in the wake of the 9-11
terrorist attacks. Fortunately, because of its Beige Book efforts, the Federal Reserve had an established
network of business contacts to draw on–a network covering a wide range of industries and extending
across the entire country. Based on long experience, the contacts were confident that they could speak
freely, without fear that the Federal Reserve would leak firm-specific information. Because they had
spoken with these same executives on many previous occasions, in a variety of circumstances, Federal
Reserve analysts and officials were able to put the information they received in proper perspective.
7. Private-Sector Expectations and Financial Asset Prices
Fourth on Goldman Sachs’ list of market-moving releases–after the PMI and the GDP and
payroll-employment reports–is the Conference Board’s Consumer Confidence index. The Consumer
Confidence index is based partly on household expectations of future economic conditions. Privatesector expectations are a main ingredient in financial asset prices, as well, and obviously completely drive
survey measures of household inflation expectations and professional forecasters’ inflation, output, and
employment predictions. An economic indicator that reflects expectations, which is not subject to
revision, and which–in the case of asset prices–is available daily or even minute-to-minute, would seem
to be ideal.
In practice, sorting out the policy implications of movements in asset prices and private-sector
forecasts is problematic. Movements in asset prices due to changes in expectations are difficult to
distinguish from those due to changes in liquidity and risk premia. Private-sector forecasts show the
outcomes that people feel are most likely, but say little about the confidence with which these beliefs are
held. Finally, policymakers may have information relevant to the economic outlook that is superior to
that possessed by the private sector. Most obviously, policymakers likely have better information about
how policy will react to various contingencies. There is always a question, then, about whether a shift in
private-sector expectations signals new private-sector information that is deserving of the monetary
authority’s attention or, instead, reflects a possibly unwarranted change in beliefs about the future conduct
of policy. Chances for misinterpretation are minimized if the monetary authority effectively
communicates its objectives and its plans for attaining them (Bernanke 2004).
8. Summary and Conclusions
Statistical reports prepared by government agencies are both better and worse than we give them
credit for being. Even the earliest government reports are surprisingly difficult to second-guess.
8
Beige Books are, of course, never revised, and the PMI is revised only when seasonal factors are re-estimated.
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Apparently, government statisticians rarely simply extrapolate from available source data to arrive at their
estimates. Difficulties arise, however, when government data are used to estimate forecasting models.
Contrary to conventional econometric practice, it is not safe to mix the lightly revised data that are typical
toward the end of most sample periods with the heavily revised data that are typical toward the beginning.
Achieving optimal forecasting performance requires that lightly revised data be used throughout the
sample, since it is lightly revised data that inevitably will be substituted into the estimated equation when
it comes time to prepare an actual forecast. Similarly, if you believe that one series is a good leading
indicator of another, you should plot the second variable along with the government’s initial estimate of
the first, rather than estimates that may incorporate many after-the-fact revisions.
While using conventional government statistical reports for forecasting is, perhaps, less
straightforward than is commonly believed, the advantages to using anecdotal and qualitative information
are often neglected. Anecdotal and qualitative information is frequently more timely than conventional
data, is less subject to revision, and better captures emerging trends. The Federal Reserve System places
high value on anecdotal and qualitative information, and its institutional structure facilitates the flow of
this type of information to policymakers from a wide range of sectors and regions of the country. These
information channels have proven invaluable in times of crisis.
Asset prices and consensus forecasts or expectations surveys might seem to be ideal economic
indicators, but policymakers need to know what is driving changes in private-sector expectations in order
to determine the appropriate response. It may well be that the best response is simply to more effectively
communicate policymakers’ own objectives and plans.
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TABLE 1. First-Release GDP, Jobs, and Industrial Production Don’t Fit the Noise Model.
Standard Deviation
Correlation
1st Release
Revised
Noise or
News?
Revisions &
1st Release
Revisions &
Revised
Noise or
News?
GDP*
2.28
2.41
news
0.13
0.35
news
Payroll
Employment**
1.80
1.96
news
0.29
0.48
news
Industrial
Production†
4.02
4.35
news
0.13
0.40
news
Comments:
The “noise” model of first-release data predicts that they will be more variable than revised data, that the first-release data will be
negatively correlated with subsequent revisions (the difference between revised and first-release data), and that revisions will be
uncorrelated with revised data. The “news” model predicts that first-release data will be less variable than revised data, that the
first-release data will be uncorrelated with subsequent revisions, and that revisions will be positively correlated with revised data.
Noise first-release data can be second-guessed, whereas news first-release data should be taken at face value.
Notes:
* Q2/Q2 real GDP growth as first released and as it appeared after the first annual revision. (Annual revisions are coincident
with release of Q2 data.) Data run from 1966 through 2001, excluding 1975, which is a statistical outlier.
** March/March non-farm payroll employment growth as first released and as it appears today. (March is the benchmark month
for payroll employment.) Data run from 1965 through 2003.
† December/December growth in industrial production as first released and as it appears today. Data run from 1965 through
2003, excluding 1983, which is a statistical outlier.
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