Order Code RL30239
CRS Report for Congress
Received through the CRS Web
Economic Forecasts and the Budget
Updated February 1, 2005
Brian W. Cashell
Specialist in Quantitative Economics
Government and Finance Division
Congressional Research Service ˜ The Library of Congress

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Economic Forecasts and the Budget
Summary
Outlays, revenues, and the federal budget surplus (or deficit) for a given year
depend on both economic performance and fiscal policy. (The budget surplus is
outlays minus revenues for a given fiscal year. The deficit is simply a negative
surplus). Slower economic growth means lower incomes and tax receipts, which
tend to reduce a federal budget surplus or increase a budget deficit. Slower growth
also means increased outlays for income support programs, which tends to reduce a
federal budget surplus or increase a budget deficit. Faster economic growth means
higher tax receipts and lower outlays, raising a federal budget surplus or decreasing
a deficit.
Early each year, the Office of Management and Budget (OMB) and the
Congressional Budget Office (CBO) release economic forecasts for the coming year,
as required by law. These forecasts are coordinated with the release of the
President’s proposed budget for the coming fiscal year. Projections of revenues,
outlays, and the federal budget surplus (or deficit) are based, in part, on these
economic forecasts.
There are a number of measures that can be used to determine the relative
accuracy of an economic forecast, and assess whether a particular forecaster’s
projections tend to be optimistic or pessimistic. These measures are “mean error,”
“mean absolute error,” and “root mean squared error.”
This report uses these “summary measures of error” to assess the forecasting
records of OMB, CBO, and the Blue Chip consensus forecast between 1982 and
2004. The Blue Chip forecast was selected as representative of private forecasters
because, as a consensus, or average, forecast, it would be at least as accurate as about
half of the 50 or so forecasts that go into it. The economic variables that are
examined here are real GDP growth, consumer price inflation, the unemployment
rate, and short-term interest rates.
With respect to the accuracy of past economic forecasts, no one forecaster
examined here emerged as more reliable or more accurate than the others. Neither
does there appear to be any significant pattern of excessive optimism or pessimism
in any of the forecasts.
Future forecasts will undoubtedly continue to be characterized by errors.
Revenue projections, for example, will turn out to have been too high one year or too
low in another. Over the longer run, however, the evidence suggests that there is no
statistically significant bias in forecast errors. Future surpluses, or deficits, may turn
out to be higher or lower than expected because of imperfect economic forecasts.
But, forecast errors are not likely to be a factor in longer run budget trends. This
report will be updated as economic developments warrant.

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Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Summary Measures of Forecast Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
The Forecasting Record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Budget Implications of Economic Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . 9
Economic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Inflation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
List of Tables
Table 1. Forecasts of Growth in Real GNP/GDP, 1982 - 2004 . . . . . . . . . . . . . . . 5
Table 2. Forecasts of Consumer Price Inflation, 1982 - 2004 . . . . . . . . . . . . . . . . 6
Table 3. Forecasts of the Unemployment Rate, 1982 - 2004 . . . . . . . . . . . . . . . . . 7
Table 4. Forecasts of the 91-day Treasury Bill Rate, 1982 - 2004 . . . . . . . . . . . . . 8
Table 5. Estimated Effects on the Projected Budget of 0.1% Faster
Rate of Economic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Table 6. Estimated Effect on Budget Projections of 1% Faster Rate of
Inflation and 1% Higher Nominal Interest Rates . . . . . . . . . . . . . . . . . . . . . 10

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Economic Forecasts and the Budget
Outlays, revenues, and the federal budget surplus (or deficit) for a given year
depend on both economic performance and fiscal policy. (The budget surplus is
outlays minus revenues for a given fiscal year. A deficit is simply a negative
surplus). Slower economic growth means lower incomes and tax receipts, which
tend to reduce a federal budget surplus or increase a budget deficit. Slower growth
also means increased outlays for income support programs, which tends to reduce a
federal budget surplus or increase a budget deficit. Faster economic growth means
higher tax receipts and lower outlays, raising a federal budget surplus or reducing a
deficit.
Policy decisions made on the basis of optimistic projections may have to be
adjusted down the road if and when those projections are not realized. If forecasts
of economic growth are persistently optimistic, then projections of budget surpluses
will tend to be too high. This report examines the accuracy of economic forecasts
used to prepare projections of the federal budget, and discusses the influence those
forecasts have on the budget projections.
Introduction
Early each year, the Office of Management and Budget (OMB) and the
Congressional Budget Office (CBO) release economic forecasts for the coming year,
as required by law.1 These forecasts are coordinated with the release of the
President’s proposed budget for the coming fiscal year, as required by law.
Projections of revenues, outlays, and the federal budget surplus (or deficit) are based,
in part, on these economic forecasts.2
Given the current state of the art, economic forecasts will err. But, whether a
forecaster tends to be overly optimistic or pessimistic cannot be determined on the
basis of a single year’s forecast. This report examines and compares the accuracy of
past OMB economic forecasts with those of CBO, as well as those of a third private
1 Section 202(f) of the Congressional Budget Act of 1974 requires CBO to produce baseline
projections of the federal budget. Title 31, Section 1105 of the U.S. Code specifies the
requirements of the President’s budget proposal. OMB and CBO both also release a second
forecast in the summer. The most recent OMB forecast can be found on the Internet at
[http://www.whitehouse.gov/omb/]. The most recent CBO forecast can be found at
[http://www.cbo.gov/].
2 OMB assumes in its January economic forecast that the budget will be enacted as
presented. That may explain some differences between the OMB forecast and other
economic forecasts.

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CRS-2
forecast, the Blue Chip Economic Indicators.3 The Blue Chip (BC) forecast is an
average of about 50 private forecasts.4 Because it is a consensus forecast, it is a good
standard for assessing the accuracy of the OMB and CBO forecasts.5
Economic forecasts are used to make budget projections, but those budget
projections depend not just on economic forecasts, but also on assumptions regarding
future tax and spending policy. Economic forecasts and related budget projections
are interdependent.6 Just as budget projections are based on assumptions about the
economic outlook, changes in budget policy can have an effect on the economic
outlook. OMB and CBO deal with this interdependence in slightly different ways.
OMB’s economic projections assume that the budget is enacted as it is introduced.
CBO’s economic projections are based on a current services budget, which assumes
that tax and spending policies remain unchanged throughout the forecast. This
difference in approach may explain some of the differences between OMB and CBO
forecasts shown below.7
Summary Measures of Forecast Error
There are three “summary measures of error” that can be used to examine
economic forecasting records.8 The first is “mean error.” Mean error is simply the
average of all of the forecast errors.9 A large positive (negative) mean error would
indicate a tendency for forecasts of a given variable to be too high (low). A
significantly large mean error, either positive or negative, might indicate a propensity
towards overly optimistic or pessimistic forecasts, depending on the particular
variable. A small mean error can be misleading, however, because large negative
errors may be offset by equally large positive errors.
The second measure of forecasting error is “mean absolute error.” Mean
absolute error is the average of the absolute value of all of the forecasting errors. It
measures how far off a forecast was regardless of whether it was too high or too low.
3 The Blue Chip Economic Indicators are available by private subscription. It is published
by Aspen Publishers Inc. of Alexandria, Virginia.
4 Individual contributors to the Blue Chip forecast change from time to time.
5 For a detailed discussion of the nature of consensus forecasts, see Stephen K McNees,
“Consensus Forecasts: Tyranny of the Majority?,” New England Economic Review,
Nov./Dec. 1987, pp.15-21.
6 For this reason, it is difficult after the fact to explain why actual budget amounts differ
from earlier projections. In part, it may have to do with economic forecast errors, but it may
also be a result of changes in policy.
7 For current information on the federal budget, see CRS Report RL31784, The Budget for
Fiscal Year 2004, by Philip D. Winters. For current information on economic conditions
and the economic outlook, see CRS Report RL30329, Current Economic Conditions and
Selected Forecasts, by Gail Makinen.
8 A summary measure of error is an attempt to measure forecast accuracy in a single statistic.
9 Individual forecast errors are calculated by subtracting the measured actual historical value
from the one that was forecast.

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CRS-3
Taking the absolute value eliminates the problem of negative and positive errors
offsetting each other.
The third measure is “root mean squared error.” It is calculated by taking the
square root of the average of the squares of the errors. Squaring the errors avoids the
problem of negative and positive errors offsetting each other. In addition, it places
a larger weight on large errors. This measure helps to point out those forecasters
who, when wrong, miss by a lot.10
Standard deviations for forecast errors are also useful here. Standard deviation
is a measure of the dispersion of a set of numbers about their mean. Used here, it
gives an estimate of the variation in individual errors. It helps to identify those errors
that are significant, rather than just a matter of chance. If the mean error is
substantially larger than the standard deviation, that could constitute evidence of bias
in the forecast.
The Forecasting Record
The OMB, CBO, and Blue Chip forecasts made from 1982 through 2004 are
compared here using each of the three summary measures of error. Measures of
standard deviation are presented as well. In each case, the forecasts examined are
those that were released early on in the year forecasted.11
For each of these forecasts, four variables are examined: growth in real gross
national product (GNP) or gross domestic product (GDP),12 the change in the
consumer price index (CPI), the civilian unemployment rate, and the interest rate on
three-month Treasury bills.13
10 To see this point, consider two forecasts for two time periods. The first forecast had
errors of 2 and 2. The second forecast has errors of 0 and 4. In both cases the mean error
is 2, suggesting equal accuracy. The first forecast also has a root mean squared error of two.
However, the root mean squared error of the second forecast is 2.8. If large errors are
considered critical, then the first forecast might be considered superior.
11 In both 1981 and 1989, with the change in presidential administrations, OMB released two
forecasts. For 1981, the last forecast released by the Carter Administration was used. For
1989, the last forecast released by the Reagan Administration was used. The Blue Chip
forecasts compared here were all published in January.
12 In late 1991, the Bureau of Economic Analysis of the Commerce Department began
emphasizing GDP as the summary measure of the level of economic activity in the United
States. See CRS Report 93-570, Gross National Product or Gross Domestic Product: What
Difference Does It Make? by Brian W. Cashell.
13 At one time, the Administration forecast the total unemployment rate which is, on
average, 0.1 percentage point below the civilian unemployment rate. To allow the
comparison shown here to be consistent, some administration forecasts for the
unemployment rate have been adjusted to reflect the actual difference between the total and
civilian unemployment rates in each year. The administration forecast of the unemployment
rate shown here is of the civilian unemployment rate. Further, interest rates, as forecast by
the administration, are assumed to follow the rate of inflation. That is, when the inflation
(continued...)

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CRS-4
Actual and forecast values for these variables for each of the last 23 years are
shown in Tables 1 through 4. Individual differences are calculated by subtracting the
actual from the forecasted value. A negative difference means that a particular
forecast turned out to be too low. A positive difference means that a particular
forecast turned out to be too high.14
In any given year, and for any particular variable, one forecast may have proven
superior to either of the other two. In cases where one forecaster’s projection proved
to be off by a substantial amount, the other two forecasts were also off by relatively
large amounts. In other words, no one forecaster tended to make major errors more
often than the others. To get a picture of the overall accuracy of these forecasts, the
summary measures are more useful.
At the bottom of each table, summary measures of error are presented for each
forecaster. These figures suggest that no one of the three forecasters examined here
stands out as either substantially inferior or superior to the others.
All of the forecasters showed a slight tendency to underpredict the real growth
rate, although OMB’s average error was slightly less than either CBO or the Blue
Chip consensus. Since 1995, all three forecasters have tended to err on the low side
with respect to growth in real GDP. The overall mean error figures are not large
enough to be interpreted as evidence of bias in any of the three forecasts.15 The other
two summary measures of error in forecasts of real growth, mean absolute error, and
root mean squared error suggest that the three forecasters did about equally well.
With respect to inflation, forecasts of the consumer price index (CPI) were
compared. There are two CPIs — the consumer price index for urban wage earners
and clerical workers (CPI-W) and the consumer price index for all urban consumers
(CPI-U). Prior to 1992, OMB forecast the CPI-W because it is the index used to
make cost of living adjustments to Social Security benefits. Since then, all three have
forecast the CPI-U. All three forecasters tended to overestimate future inflation,
although the error was not statistically significant.
The summary measures of error suggest a virtual tie with regard to forecasting
the unemployment rate. There was no indication of a statistically significant bias in
these forecasts.
13 (...continued)
rate rises or falls in the projection, interest rates are assumed to rise and fall by equal
amounts.
14 GNP and GDP data are subject to periodic revisions. In other words, the ‘actual’ data is
subject to change, and so the apparent accuracy of forecasts is also subject to change.
Actual data shown here are the latest estimates available as of January 2003. Although this
may be unfair since definitions and methodologies have changed in the actual data since the
forecasts were made, it is at least equally unfair to the three forecasts.
15 For the mean error estimate to lead to a conclusion of systematic bias in the forecasts it
would have to be at least two standard deviations greater than, or less than, zero. In other
words, the error cannot be considered to be statistically significant unless it is at least larger
than the standard deviation which measures the overall variation in the error terms.

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CRS-5
The OMB forecasts of short-term interest rates seem to have been slightly better
than the other two over the period examined here, but the differences in the measures
of forecast error were not significantly different. Neither was there any statistically
significant tendency to either over- or underpredict interest rates.
Table 1. Forecasts of Growth in Real GNP/GDP, 1982 - 2004
OMB
CBO
Blue Chip
Actuala
Forecast
Difference
Forecast
Difference
Forecast
Difference
1982
-1.9
0.2
2.1
-0.1
1.8
0.3
2.2
1983
4.4
1.4
-3.0
2.1
-2.3
2.5
-1.9
1984
7.1
5.3
-1.8
5.4
-1.7
5.3
-1.8
1985
3.8
3.9
0.1
3.5
-0.3
3.3
-0.5
1986
3.2
3.4
0.2
3.2
0.0
3.0
-0.2
1987
3.3
3.1
-0.2
2.8
-0.5
2.4
-0.9
1988
4.2
2.9
-1.3
2.3
-1.9
2.2
-2.0
1989
3.5
3.2
-0.3
2.9
-0.6
2.7
-0.8
1990
2.0
2.4
0.4
1.7
-0.3
1.7
-0.3
1991
-0.3
-0.3
0.0
0.0
0.3
-0.1
0.2
1992
3.3
1.5
-1.8
1.6
-1.7
1.6
-1.7
1993
2.7
3.1
0.4
2.8
0.1
2.9
0.2
1994
4.0
3.1
-0.9
2.9
-1.1
3.0
-1.0
1995
2.5
2.8
0.3
3.1
0.6
3.1
0.6
1996
3.7
2.2
-1.5
2.0
-1.7
2.2
-1.5
1997
4.5
2.2
-2.3
2.3
-2.2
2.3
-2.2
1998
4.2
2.4
-1.8
2.7
-1.5
2.5
-1.7
1999
4.5
2.4
-2.1
2.3
-2.2
2.4
-2.1
2000
3.7
3.3
-0.4
3.3
-0.4
3.6
-0.1
2001
0.8
2.4
1.6
2.4
1.6
2.6
1.8
2002
1.9
0.7
-1.2
0.8
-1.1
1.0
-0.9
2003
3.0
2.9
-0.1
2.5
-0.5
2.8
-0.2
2004
4.4
4.4
0.0
4.8
0.4
4.6
0.2
Summary measures of error
Mean error
-0.59
-0.66
-0.63
Mean absolute error
1.03
1.08
1.09
Root mean squared error
1.36
1.31
1.33
Standard deviation
1.22
1.13
1.16
Sources: Department of Commerce, Bureau of Economic Analysis; Congressional Budget Office; Office of
Management and Budget; Blue Chip Economic Indicators.
a. Forecasts are of GNP prior to 1992 and of GDP in 1992 and from then on.

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CRS-6
Table 2. Forecasts of Consumer Price Inflation, 1982 - 2004
Actual
OMBa
CBO
Blue Chip
CPI-W
CPI-U
Forecast
Difference
Forecast
Difference
Forecast
Difference
1982
6.0
6.2
7.3
1.3
7.5
1.3
7.8
1.6
1983
3.0
3.2
4.9
1.9
3.8
0.6
5.0
1.8
1984
3.5
4.3
4.4
0.9
4.5
0.2
5.0
0.7
1985
3.5
3.6
4.1
0.6
3.8
0.2
4.2
0.6
1986
1.6
1.9
3.5
1.9
3.4
1.5
3.6
1.7
1987
3.6
3.6
3.0
-0.6
3.5
-0.1
3.2
-0.4
1988
4.0
4.1
4.3
0.3
4.5
0.4
4.2
0.1
1989
4.8
4.8
3.8
-1.0
4.9
0.1
4.7
-0.1
1990
5.2
5.4
3.9
-1.3
4.0
-1.4
4.2
-1.2
1991
4.1
4.2
5.2
1.1
4.9
0.7
4.8
0.6
1992
2.9
3.0
3.0
0.0
3.3
0.3
3.3
0.3
1993
2.8
3.0
3.1
0.1
3.0
0.0
3.1
0.1
1994
2.5
2.6
2.8
0.2
2.7
0.1
2.8
0.2
1995
2.9
2.8
3.1
0.3
3.1
0.3
3.3
0.5
1996
2.9
3.0
2.8
-0.2
2.8
-0.2
2.8
-0.2
1997
2.3
2.3
2.7
0.4
2.9
0.6
2.9
0.6
1998
1.3
1.6
2.1
0.5
2.2
0.6
2.2
0.6
1999
2.2
2.2
2.2
0.0
2.5
0.3
2.0
-0.2
2000
3.5
3.4
2.6
-0.8
2.5
-0.9
2.5
-0.9
2001
2.7
2.8
2.7
-0.1
2.8
0.0
2.6
-0.2
2002
1.4
1.6
1.8
0.2
1.8
0.2
1.7
0.1
2003
2.2
2.3
2.2
-0.1
2.3
0.0
2.2
-0.1
2004
2.6
2.7
1.4
-1.3
1.6
-1.1
1.7
-1.0
Summary measures of error
Mean error
0.19
0.16
0.23
Mean absolute error
0.66
0.48
0.60
Root mean squared error
0.87
0.66
0.80
Standard deviation
0.85
0.64
0.76
Sources: Department of Labor, Bureau of Labor Statistics; Congressional Budget Office; Office of Management
and Budget; Blue Chip Economic Indicators.
a. OMB forecast the CPI-W prior to 1992 and the CPI-U in 1992 and from then on.

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CRS-7
Table 3. Forecasts of the Unemployment Rate, 1982 - 2004
OMBa
CBO
Blue Chip
Actual
Forecast
Difference
Forecast
Difference
Forecast
Difference
1982
9.7
8.9
-0.8
8.9
-0.8
8.6
-1.1
1983
9.6
10.9
1.3
10.6
1.0
10.3
0.7
1984
7.5
7.9
0.4
7.8
0.3
8.0
0.5
1985
7.2
7.1
-0.1
7.1
-0.1
7.2
0.0
1986
7.0
6.8
-0.2
6.7
-0.3
7.0
0.0
1987
6.2
6.8
0.6
6.6
0.4
7.0
0.8
1988
5.5
5.9
0.4
6.2
0.7
6.2
0.7
1989
5.3
5.3
0.0
5.5
0.2
5.4
0.1
1990
5.6
5.4
-0.2
5.6
0.0
5.6
0.0
1991
6.8
6.8
0.0
6.8
0.0
6.5
-0.3
1992
7.5
6.9
-0.6
6.9
-0.6
6.8
-0.7
1993
6.9
7.1
0.2
7.1
0.2
7.0
0.1
1994
6.1
6.5
0.4
6.4
0.3
6.4
0.3
1995
5.6
5.8
0.2
5.5
-0.1
5.6
0.0
1996
5.4
5.7
0.3
5.8
0.4
5.8
0.4
1997
4.9
5.3
0.4
5.3
0.4
5.4
0.5
1998
4.5
4.9
0.4
4.8
0.3
4.8
0.3
1999
4.2
4.8
0.6
4.6
0.4
4.7
0.5
2000
4.0
4.2
0.2
4.1
0.1
4.1
0.1
2001
4.7
4.4
-0.3
4.4
-0.3
4.4
-0.3
2002
5.8
5.9
0.1
6.1
0.3
6.1
0.3
2003
6.0
5.7
-0.3
5.9
-0.1
5.9
-0.1
2004
5.5
5.6
0.1
5.8
0.3
5.7
0.2
Summary measures of error
Mean error
0.13
0.13
0.13
Mean absolute error
0.35
0.33
0.35
Root mean squared error
0.45
0.41
0.45
Standard deviation
0.43
0.39
0.44
Sources: Department of Labor, Bureau of Labor Statistics; Congressional Budget Office; Office of Management
and Budget; Blue Chip Economic Indicators.
a. Prior to 1992 OMB forecast the total unemployment rate. The OMB forecasts shown here have been adjusted
to account for the difference between the total and civilian unemployment rates.

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CRS-8
Table 4. Forecasts of the 91-day Treasury Bill Rate, 1982 - 2004
OMB
CBO
Blue Chip
Actual
Forecast
Difference
Forecast
Difference
Forecast
Difference
1982
10.6
11.7
1.1
12.0
1.4
10.8
0.2
1983
8.6
8.0
-0.6
6.8
-1.8
7.7
-0.9
1984
9.5
8.5
-1.0
8.9
-0.6
8.8
-0.7
1985
7.5
8.1
0.6
8.3
0.8
8.6
1.1
1986
6.0
7.3
1.3
6.8
0.8
7.1
1.1
1987
5.8
6.8
1.0
6.6
0.8
5.4
-0.4
1988
6.7
5.3
-1.4
6.2
-0.5
6.0
-0.7
1989
8.1
6.3
-1.8
7.9
-0.2
7.8
-0.3
1990
7.5
6.7
-0.8
6.9
-0.6
7.1
-0.4
1991
5.4
6.4
1.0
6.6
1.2
6.3
0.9
1992
3.4
4.1
0.7
4.4
1.0
4.2
0.8
1993
3.0
3.7
0.7
3.1
0.1
3.5
0.5
1994
4.3
3.4
-0.9
3.5
-0.8
3.4
-0.9
1995
5.5
5.9
0.4
6.2
0.7
6.2
0.7
1996
5.0
4.9
-0.1
4.9
-0.1
5.0
0.0
1997
5.1
5.0
-0.1
5.0
-0.1
5.2
0.1
1998
4.8
5.0
0.2
5.3
0.5
5.1
0.3
1999
4.6
4.2
-0.4
4.5
-0.1
4.3
-0.3
2000
5.8
5.2
-0.6
5.4
-0.4
5.6
-0.2
2001
3.4
5.3
1.9
4.8
1.4
5.4
2.0
2002
1.6
2.2
0.6
2.2
0.6
2.1
0.5
2003
1.0
1.6
0.6
1.4
0.4
1.6
0.6
2004
1.4
1.3
-0.1
1.3
-0.1
1.3
-0.1
Summary measures of error
Mean error
0.10
0.19
0.17
Mean absolute error
0.78
0.65
0.60
Root mean squared error
0.92
0.80
0.74
Standard deviation
0.91
0.77
0.72
Sources: Board of Governors of the Federal Reserve System; Congressional Budget Office; Office of
Management and Budget; Blue Chip Economic Indicators.

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CRS-9
Budget Implications of Economic Forecasts
Economic forecasts have a substantial effect on the budget outlook. For
example, the faster the economic growth rate is forecast to be, the higher projections
of revenues will be and the lower projections of outlays will be, resulting in a larger
budget surplus projection or a smaller budget deficit projection. Each year, CBO
publishes estimates that go out 10 years of the effect that alternative economic
forecasts would have on their projections of revenues, outlays, and the budget surplus
(or deficit).
Economic Growth. Changes in forecasts of economic growth have a greater
effect on revenues than they do on outlays. With respect to revenues, faster
economic growth means higher incomes and higher tax payments. Outlays are also
reduced, because borrowing needs are reduced, the debt grows less rapidly, and
interest payments are smaller. Slower economic growth, on the other hand, would
tend to reduce the projected path of revenues, increase projected outlays and the
projected surplus would tend to be smaller, or the deficit would tend to be larger.
Table 5 presents the estimated effects of changes in assumptions about
economic growth on projections of revenues, outlays, and the surplus through 2015.
CBO estimates the effect of 0.1% faster growth in real GDP, and the change is
assumed to persist over the entire interval. In other words, the change is not
considered to be cyclical but rather is assumed to be a change in the long-run trend
rate of growth of the economy. Because the change in growth is not a cyclical
phenomenon but is related to productivity, employment levels are assumed to remain
unchanged. According to these estimates, for each 0.1% increase in economic
growth between 2005 and 2015, the cumulative budget surplus over that period
would rise by $262 billion. Or, in the case of deficits, the cumulative deficit would
fall by $262 billion.
Table 5. Estimated Effects on the Projected Budget
of 0.1% Faster Rate of Economic Growth
(all figures in billions of dollars)
Fiscal
Change in
Change in
Net Change -
Year
Revenues
Outlays
Surplus
2005
1
a
1
2006
3
a
3
2007
6
a
7
2008
9
-1
10
2009
13
-2
15
2010
17
-3
20
2011
22
-4
26
2012
27
-6
33
2013
33
-8
40
2014
38
-11
49
2015
44
-14
58
Source: Congressional Budget Office.
a. Between -$500 million and $500 million.

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CRS-10
Inflation. Inflation forecasts also have a substantial effect on the budget
outlook. A forecast of faster inflation will raise projected outlays because a number
of programs, such as Social Security, are tied directly to changes in the consumer
price index. Outlays would also increase because of the higher prices for goods and
services. Faster inflation also increases revenues simply because of the increase in
both nominal income and the associated tax payments. CBO assumes that, if
inflation is higher, then nominal interest rates would also be higher by an equal
amount. Higher interest rates raise the cost of financing the outstanding debt, and
also increase interest income to holders of federal securities. Table 6 presents the
estimated effects of changes in assumptions about inflation on projections of
revenues, outlays, and the surplus through 2015.
The long-run effect on budget projections of assuming more rapid inflation is
greater for revenues than it is for outlays. Thus, faster inflation has the direct effect
of increasing projected surpluses. Because of the larger surpluses, the outstanding
debt shrinks faster and interest outlays are also reduced. The cumulative effect of ten
years of faster inflation and higher interest rates is estimated to raise the surpluses
(reduce the deficits) by $229 billion.
Table 6. Estimated Effect on Budget Projections of 1% Faster
Rate of Inflation and 1% Higher Nominal Interest Rates
(all figures in billions of dollars)
Fiscal
Change in
Change in
Net Change -
Year
Revenues
Outlays
Surplus
2005
12
14
-3
2006
35
44
-9
2007
62
72
-9
2008
94
100
-6
2009
131
131
a
2010
170
164
6
2011
210
196
13
2012
262
230
32
2013
317
266
50
2014
373
305
68
2015
433
347
87
Source: Congressional Budget Office.
a. Between -$500 million and $500 million.

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CRS-11
Conclusions
Budget projections are not possible without making some basic assumptions
about the economic outlook. Projections of revenues require projections of incomes
out of which taxes are paid. Projections of outlays depend on estimates of future
inflation and interest rates. A wide range of economic variables can affect both.
With respect to the accuracy of past economic forecasts, no one forecaster
examined here appeared to be significantly more reliable or more accurate than the
others. Neither does there appear to be any significant pattern of excessive optimism
or pessimism in any of the forecasts. It appears that, to the extent that the official
forecasts have missed the mark, it is simply a reflection of the state of the art of
economic forecasting rather than an indication of bias.
Forecasts of future economic performance will undoubtedly continue to err.
Revenue projections, for example, will turn out to have been too high one year or too
low in another. A surplus may turn out to be higher or lower than was expected
because of an imperfect economic forecast. With respect to forecasts of real
economic growth, there has been a slight tendency for forecasts to be low, especially
in recent years. Inflation forecasts, meanwhile, have tended to be a little high. In
neither case, however, was the error significant. In the case of forecasts of the
unemployment rate and short-term interest rates, the evidence suggests that forecast
errors tended to balance out. While there is no basis to assume that biased forecasts
will lead to unrealistic budget projections, small deviations from the underlying
economic forecast can have significant effects on future budgets.

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