NOVEMBER 2015

CBO’s Revenue Forecasting Record

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Summary

To prepare the baseline budget projections on which much of its analysis is based, the 

Congressional Budget Office must regularly produce revenue forecasts. As a part of that 

process, the agency assesses the accuracy of its past projections and continually refines 

its methods for projecting revenues to attempt to make them more accurate. This report 

examines the accuracy of CBO’s revenue projections since 1982, the earliest year for 

which the information necessary to assess the forecasts is available. On average, the 

agency’s projections have been a bit too high—more so for projections spanning six 

years than for those spanning two—owing mostly to the difficulty of predicting when 

economic downturns will occur. The overall accuracy of CBO’s revenue projections has 

been similar to that of the projections of other government agencies.

How Accurate Have CBO’s Two-Year Revenue Projections Been?

On average, CBO has overestimated total revenues by 1.1 percent in its two-year 

projections—those that provide estimates of revenues for the fiscal year following 

the year in which they are released. A misestimate of that size in its January 2015 

baseline projection, for example, would amount to $37 billion out of the roughly 

$3.5 trillion in total revenues that CBO projected for fiscal year 2016.1

 Overestimates 

1. See Congressional Budget Office, The Budget and Economic Outlook: 2015 to 2025 (January 

2015), www.cbo.gov/publication/49892.

Notes: Unless otherwise indicated, all years referred to are federal fiscal years, which run from October 1 

to September 30 and are designated by the calendar year in which they end.

Numbers in the text and tables may not add up to totals because of rounding.

Some figures have vertical bars that indicate the duration of recessions. (A recession extends from the 

peak of a business cycle to its trough.)The historical data on revenues and gross domestic product used 

in this report are based on information available as of January 2015. Incorporating more recent data 

into this analysis would have only a very minor effect on the results. 

Supplemental data for this analysis are posted along with this report on CBO’s website (www.cbo.gov/

publication/50831).
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and underestimates offset one another in the mean error measure, so that average 

overestimate of 1.1 percent over the past three decades includes projections for 

years in the latest recession for which CBO overestimated revenues by as much as 

25 percent and projections for the late 1990s and the mid-2000s for which CBO 

underestimated revenues by nearly 10 percent (see Figure 1). The calculation of those 

errors—and of all such measures cited in this report—includes an adjustment to 

remove the estimated effects of legislation enacted after the projections were produced. 

That adjustment is necessary because the baseline projections incorporate the 

assumption that current laws governing taxes will generally not be modified by future 

legislation. 

In addition to the mean error, CBO employs two other commonly used measures to 

evaluate the accuracy of revenue projections: the root mean square error (RMSE) and 

the mean absolute error. Unlike the mean error, the mean absolute error is the average 

of the errors without regard to direction (the negative signs are removed from 

underestimates before averaging), so errors in different directions do not offset one 

another. The RMSE, the calculation of which involves squaring the errors (thus 

removing the negative signs), also measures the size of errors without regard to 

direction, but by squaring the errors, it places a greater weight on larger deviations. 

The mean absolute error is an easier measure to understand, but the RMSE may be a 

more useful measure of forecast errors for revenue projections because larger forecast 

errors may have a disproportionately greater cost for policymaking than smaller ones. 

For CBO’s two-year revenue projections made since 1982, the mean absolute error is 

5.2 percent, and the RMSE is 7.4 percent (see Table 1). A mean absolute error of that 

magnitude would correspond to an error of about $180 billion in the revenue estimate 

for 2016 that CBO released in its January 2015 baseline projections. Because a 

disproportionate share of the misestimates occurred in projections made in years 

immediately preceding recessions, both the RMSE and the mean absolute error are 

roughly one-third smaller when the four two-year projections (out of the 32 included in 

this analysis) that were produced at or near peaks in the business cycle are excluded.

The RMSE provides a useful guide for assessing the distribution of CBO’s past two-year 

projection errors. If the errors of a given set of forecasts are normally distributed around 

a mean error of zero—that is, if the misestimates are roughly symmetrically distributed 

around zero and there are more relatively small errors than large ones—about twothirds of the forecasts will have misestimates within a range of plus or minus one 

RMSE.2

 CBO’s two-year projections have had a small mean error, and they have 

misestimated revenues by small amounts more often than they have misestimated by 

large amounts. Thus, about two-thirds of those projections could be expected to have 

misestimated revenues by 7.4 percent (the RMSE of CBO’s two-year projections) or less 

2. More precisely, if the errors were normally distributed around a mean of zero, 68 percent of them 

would have an absolute value equal to or less than one RMSE.
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CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 3

in either direction; indeed, three-quarters of the misestimates fell into that range. 

Because the sample size of historical forecasts is relatively small, however, it is not 

possible to know the true distribution of the forecast errors.

By CBO’s calculation, the Administration’s forecast errors for revenues have been very 

similar to CBO’s. In its two-year forecasts over the same period (adjusted to exclude 

the effects of its proposed policy changes), the Administration also overestimated 

revenues, on average—by 1.7 percent, a little more than CBO’s average overestimate 

of 1.1 percent; the RMSE of its forecasts is 7.8 percent, very close to CBO’s RMSE of 

7.4 percent. Likewise, the accuracy of revenue projections by state governments has 

also been very similar to CBO’s for comparable sources of revenues.

How Accurate Have CBO’s Six-Year Revenue Projections Been?

The projection errors have tended to be larger for longer horizons than shorter ones. 

CBO’s six-year revenue projections—those that estimate revenues for the fifth fiscal 

year after the year in which they are released—have, on average, overestimated 

revenues by 5.3 percent. The mean absolute error of those projections is 10.4 percent, 

and the RMSE is 12.1 percent. A mean absolute error of that magnitude would 

correspond to an error of about $420 billion in the revenue estimate for 2020 that 

CBO released in its January 2015 baseline projections. The preponderance of 

overestimates over that longer horizon results in part from the fact that many of the sixyear periods encompassed a recession that reduced economic activity and tax revenues 

below projected amounts.

The RMSE is not as helpful for analyzing the distribution of the errors for CBO’s six-year 

projections as it is for the two-year projection errors because the six-year projections 

have a larger mean error than the two-year projections and have resulted in about as 

many relatively large errors as small ones. The mean absolute error and the RMSE show 

some signs of stabilizing at the six-year horizon, measuring not much higher than those 

calculated for the five-year horizon. However, the general accuracy of CBO’s forecasts 

extending beyond six years may not become clearer until well into the future, when 

enough such forecasts have been produced to allow for a comprehensive assessment.3

CBO’s six-year forecasts of revenues as a share of gross domestic product (GDP) have 

an RMSE of 1.1 percentage points and a mean absolute error of 0.9 percentage 

points. In CBO’s January 2015 baseline projections, revenues for 2020, the sixth year 

of the projection, total 18.0 percent of GDP. On the basis of the mean absolute error of 

3. Before 1996, CBO’s baseline typically covered six years: the year in progress at the time of the 

projections and the subsequent five years. In 1996, CBO adopted its current practice of releasing 

projections that cover the current year and the next 10 years. The agency first published 

supplemental projections covering that longer period in 1992, but those projections did not include 

information about the source of changes from the previous projections; the effects of recently 

enacted legislation for the extended period were not published until 1996. The limited data for those 

extended projections do not allow for a general assessment of forecast quality.
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CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 4

past forecasts, revenues for that year might be expected to be as low as 17.1 percent of 

GDP or as high as 18.9 percent if there are no changes to current law. (The actual 

error for that particular projection might still fall outside that range.)

How Efficiently Has CBO Incorporated New Information?

CBO has tended to revise consecutive revenue forecasts in the same direction, 

suggesting that the agency does not efficiently incorporate new information into its 

forecasts. That tendency was less pronounced in the past 15 years than it was in the 

previous period, although the limited number of forecasts that can be assessed makes 

it difficult to conclude that CBO has improved its use of new information. That 

tendency, furthermore, has varied significantly over the entire history of CBO’s 

forecasts, and CBO’s forecast accuracy would not have been systematically improved 

had the agency incorporated into its forecasts what was known at the time about that 

tendency; such modifications would have over adjusted the forecasts in many cases.

What Factors Have Contributed to Forecast Errors in CBO’s Revenue Projections?

All revenue forecast errors can be attributed either to errors in projections of GDP or to 

errors in projections of revenues raised as a percentage of GDP. The largest forecast 

errors are associated with revenue projections produced for years during which 

recessions occurred and GDP ended up being significantly lower than expected. Those 

substantial revenue shortfalls have also stemmed from other factors, including 

unexpected declines relative to GDP in certain types of income—most notably wages 

and salaries, corporate profits, and capital gains realizations—and declines in the 

share of overall income earned by the highest-earning taxpayers, which push down the 

effective tax rate (that is, total taxes as a percentage of total income). To a lesser extent, 

those same factors and others, working in both directions, have contributed to forecast 

errors in years without recessions.

At the two-year horizon, the RMSE for revenues as a percentage of GDP has been a 

little over twice as large as the RMSE for GDP. By the six-year horizon, the RMSE of each 

of those two measures is very similar. The projection errors for GDP have tended to be 

larger for longer forecast horizons, increasing steadily with each additional year of the 

horizon through the six-year horizon, whereas forecast errors for revenues as a 

percentage of GDP have tended to be larger for each year added to the horizon 

through the first four years but then to decline slightly at the five- and six-year horizons. 

It is unclear whether those patterns would continue in projections extending beyond six 

years. 

Errors in revenue forecasts can also be broken down by source of revenues, and the 

projection errors for each of those specific sources vary widely. Of the seven categories 

of revenue sources, the forecasts of corporate income taxes exhibited the largest errors 

(measured as percentages of the actual revenues from the specific source), and those 

of payroll taxes, the smallest. Although the misestimates for projections of individual 
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CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 5

income taxes fall in the middle of the range of errors for the source categories, the 

projections of such taxes, which were the largest source of federal revenues each year 

over the historical period, have contributed the most to CBO’s forecast errors for total 

revenues, followed by those of corporate income taxes (the third largest revenue 

source) and payroll taxes (the second largest). The Administration’s forecasts for those 

revenue sources have had errors similar to those in CBO’s forecasts, as have states’ 

projections for individual income taxes and corporate income taxes, the main 

comparable sources of states’ revenues. 

CBO’s Methods for Projecting Revenues

CBO’s methods for projecting revenues reflect the key assumption that underlies the 

baseline projections—namely, that current laws governing taxes will generally remain 

unchanged. Therefore, the methods involve more than simply using the past 

relationship between receipts and macroeconomic measures like GDP as a guide 

because that relationship has been significantly altered over time by changes to 

provisions of tax law.

Instead, CBO projects revenues largely by identifying the macroeconomic variables in 

its economic forecasts that constitute the bases on which the various federal taxes are 

imposed and applying to those bases the appropriate effective tax rates—which are 

often much different than the statutory tax rates set by law. CBO therefore models each 

major revenue source separately and projects total revenues by summing up the 

projections of the separate sources. The macroeconomic measures used to project 

revenues include wages and salaries and certain business income, all of which 

influence both individual income taxes and payroll taxes; asset prices, which affect the 

income tax base (through capital gains realizations and distributions from tax-deferred 

retirement accounts) as well as the base for estate and gift taxes; and corporate profits, 

the key determinant of corporate income taxes. The effective tax rates are modeled in 

different ways, including using a microsimulation approach that projects individual 

income and payroll taxes on the basis of data for individual tax-filing units as well as a 

model that projects corporate income taxes on the basis of more aggregate measures.

When projecting revenues, CBO also assesses how tax collections from recent months 

deviated from the amounts that were estimated for the course of the full year by its 

various models. That assessment includes determining whether the factors that may 

explain such deviations, which typically are not known until more detailed information 

becomes available in the future, are temporary or are likely to persist. 

As detailed data about tax collections become available, CBO modifies its 

forecasting models and methods appropriately. Over the three decades covered in 

this report, CBO made numerous adjustments to its methods to reflect developments 

revealed by the data. For example, more than a decade ago, CBO incorporated into 

its income and payroll tax projections the expectation that earnings of higher-income 
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CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 6

taxpayers would grow more quickly than those of other taxpayers, continuing a trend 

that has been evident since the late 1970s. More recently, CBO modified its 

projection methods when it found that past deviations in collections from individual 

income taxes that were not caused by inaccuracies in the economic forecast tended 

to be temporary rather than permanent.4 That conclusion appears to be broadly 

consistent with this report’s finding that errors in projections of revenues as a 

percentage of GDP tend to flatten and then decline somewhat for progressively 

longer forecasting horizons through the six-year horizon.

CBO’s Method for Assessing Forecast Accuracy

Both CBO and the Administration have published a number of forecasts over the years 

that, with the adjustments described below, can be analyzed for accuracy and 

compared with one another. The quality of those forecasts can be measured in various 

ways, but in this assessment, CBO focuses primarily on two characteristics: statistical 

bias (how consistently high or low errors are) and accuracy (how large errors are in 

either direction). Although assessing past forecasts is an important part of the 

forecasting process, a number of factors limit the extent to which such assessments can 

improve the accuracy of future projections.

Sources of Data

This evaluation uses single-year estimates from the revenue projections that CBO has 

released near the beginning of each calendar year, typically in January or February, 

since 1982. Each of those projections covered the fiscal year in progress and at least 

the subsequent five years. For example, in February 2013, CBO released projections of 

revenues for fiscal year 2013 (the one-year projection), fiscal year 2014 (the two-year 

projection), and for each of the next nine years (see Figure 2). The Treasury Department 

reports the actual amounts of revenues for the fiscal year (subject to later revisions, 

which are typically small) each October, shortly after the fiscal year ends.

Although CBO has made economic and budget projections since shortly after it began 

operating in 1975, the projections produced before 1982 are not comparable with 

later forecasts because detailed data on the effects of subsequently enacted legislation 

are not available; those early forecasts are therefore excluded from this analysis. The 

remaining sample of forecast inaccuracies is relatively small by large-scale statistical 

standards: 33 one-year forecasts (that is, forecasts for the current year), 32 two-year 

forecasts, 31 three-year forecasts, and so forth, meaning there are 28 six-year 

forecasts.

This analysis compares CBO’s projections with those released by the Administration 

since 1982, typically in February, when it submits the budget to the Congress. Each of 

4. See Congressional Budget Office, Improving CBO’s Methodology for Projecting Individual Income 

Tax Revenues (February 2011), www.cbo.gov/publication/22007.
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CBO’s baseline projections is by design a neutral benchmark against which proposed 

policy changes can be measured; the agency’s projections are therefore based on the 

assumption that laws generally remain the same. In contrast, the official budget 

projections prepared by the Administration include the estimated effects on revenues of 

its proposed policies that had not been enacted at the time the projections were made. 

To make those projections comparable with CBO’s revenue projections for the same 

period, CBO removed the effects of the proposed policies from the Administration’s 

revenue projections by subtracting the Administration’s estimates of the effects of those 

proposed policies, which are published separately.5

Actual revenues are, however, affected by legislation that was enacted after the 

projections were made. To remove the effects of such legislation from its measure of 

forecast errors (the difference between projected and actual revenues), CBO adjusted 

the projections to incorporate the effect of the subsequently enacted legislation on 

revenues before comparing them to actual revenues. The Administration does not 

generally publish estimates of the revenue effects of legislation that is enacted after it 

releases its projections, so CBO used the Congressional estimates of the revenue 

effects—generally the estimates prepared by the staff of the Joint Committee on 

Taxation near the time of the legislation’s enactment—to adjust the Administration’s 

projections as well as its own.6

 The adjustment for subsequent legislation is imperfect 

because the estimates of the effects of that legislation are themselves imperfect.

To calculate forecast errors, actual revenues were subtracted from those adjusted 

revenue projections, and the difference was divided by actual revenues. Thus, the 

forecast errors are expressed as percentages of actual revenues, with positive values 

representing overestimates and negative values underestimates. CBO’s assessment of 

5. The Administration’s economic projections include the effects of the proposals, but the 

Administration does not include changes in revenues that would result from those economic effects 

in its separate revenue projections for the proposals. Therefore, the Administration’s revenue 

projections with the effects of proposed policies removed may include some economic feedback 

from the policy proposals and thus are not completely comparable with CBO’s baseline projections. 

In the past, the Administration published its own revenue projections without the effects of its 

proposed policies as its “current services baseline”; more recently, it has published those projections 

as the “Balanced Budget and Emergency Deficit Control Act baseline.” CBO used those estimates 

directly, but for some details, it used information provided separately by the Administration at the 

time that the budget was released.

6. The Administration includes a historical assessment of its projections of overall budget deficits and 

surpluses in its annual budget, but it does not separately assess the projections of revenues and 

outlays. The budget does, however, contain a separate assessment of the Administration’s two-year 

revenue projection for the most recently completed year, including an estimate of the effects of 

subsequently enacted legislation. CBO’s estimates of the Administration’s forecast errors (adjusted for 

the effects of subsequently enacted legislation) for those two-year projections were very similar to what 

the Administration reported. Therefore, for consistency, in this report CBO used its own estimates of the 

Administration’s forecast errors for all time periods. The ideal assessment of the Administration’s 

forecast accuracy would use its estimates of the effects of subsequently enacted legislation, which could 

result in findings that differ from CBO’s.
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CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 8

its January 2009 projection offers an example: That month, the agency released 

baseline projections for 2009 through 2019, estimating that revenues would total 

$2,533 billion in fiscal year 2010 (see Table 2). To assess the accuracy of that two-year 

projection, CBO adjusted it to incorporate the effects of legislation enacted between 

January 2009 and the end of fiscal year 2010. That legislation reduced revenues in 

2010 by an estimated $219 billion, most of which was attributable to the enactment of 

the American Recovery and Reinvestment Act of 2009. Adjusting the January 2009 

projection to incorporate the effects of subsequent laws yields an adjusted projection 

totaling $2,315 billion. The actual revenues in 2010—$2,163 billion—were then 

subtracted from that adjusted amount, indicating an overestimate of $152 billion, or 

7.0 percent of the actual amount (see Figure 3).

Both CBO’s and the Administration’s revenue projections include details on the 

different sources of federal revenues: individual income taxes, payroll taxes, corporate 

income taxes, excise taxes, customs duties, estate and gift taxes, and miscellaneous 

receipts, which include remittances from the Federal Reserve System, fees, and fines. In 

addition to an evaluation of the projections of total revenues, this analysis includes a 

separate evaluation for the specific projections of each source of federal revenues. 

Measuring the Quality of Forecasts

This evaluation focuses on two aspects of the quality of CBO’s forecasts: statistical bias 

and accuracy. To a lesser extent, it also assesses how efficiently CBO incorporates new 

information into its forecasts.

Statistical Bias. The statistical bias of a forecast is its tendency to err in a particular 

direction. CBO’s intent is to provide a revenue baseline that is free of such statistical 

bias—that is, one that is equally likely to be higher than actual revenues as it is to be 

lower. To measure statistical bias, CBO uses the mean error—the arithmetic average of 

the forecast errors—which is the simplest and most widely used measure of forecast 

bias. However, because the negative values of underestimates offset the positive values 

of overestimates when calculating the mean error, the measure provides an imperfect 

view of the accuracy of a forecast. For example, a number of forecasts with small 

misestimates in both directions that largely offset one another would produce a small 

mean error. But so, too, would relatively large overestimates and underestimates, as 

long as they were approximately the same magnitude and counterbalanced one 

another. 

Accuracy. The accuracy of a forecast can be measured by the degree to which a set of 

forecast values differs from the actual outcomes. Such accuracy is determined by two 

properties of a set of forecast values: bias (also known as centeredness), discussed 

above, and variance (also known as spread), the dispersion of the differences between 

the forecast values and the actual values around the average of those differences. 

Estimates that have less bias and less variance are more accurate. 
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In this evaluation, CBO used two standard measures of accuracy: the mean absolute 

error and the root mean square error. The mean absolute error is the average of the 

errors without regard to direction, so errors in different directions do not offset one 

another. The RMSE also measures the size of errors without regard to direction, but by 

squaring the errors, it places a greater weight on larger deviations.7 By design, it 

combines estimates of bias and variance into a single measure. 

Although the mean absolute error is conceptually an easier measure to understand, the 

RMSE may be a better measure of forecast errors for revenue projections because 

larger forecast errors may have a disproportionately greater cost than smaller ones by, 

for example, leading policymakers to make adjustments to tax or spending policies that 

later turn out to be unwarranted. Thus, this report focuses more on the RMSE than on 

the mean absolute error. Other summary measures of accuracy might also be 

constructed to identify and emphasize those patterns of forecast errors that most 

negatively affect policymaking.

Efficient Use of New Information. Another way to assess a forecasting record is to 

evaluate how forecasts made in one year compare with those made in subsequent 

years. If revisions to a given forecast in one direction tend to be followed by revisions in 

the same direction in the subsequent forecasts, then the forecaster may not be 

incorporating all available information that could reduce forecast errors.

Limitations of Forecast Evaluations

Despite the importance of evaluating those three characteristics of forecasts, the extent 

to which conclusions drawn from this assessment can help improve CBO’s revenue 

projections is limited for several reasons: Projection methods have changed, estimates 

of the effects of legislation are imperfect, and only a small number of forecasts are 

available to examine. 

CBO has significantly changed its methods of projecting revenues over the years. The 

agency has attempted to improve its projections by incorporating new information, and 

the characteristics of the resulting projection errors may well have changed as it has 

done so. As a result, it is difficult to learn from past forecast errors precisely how to 

prevent forecast errors in the future.

In addition, it is not possible to separately analyze the quality of baseline revenue 

projections and the quality of the estimates that CBO later incorporates to account for 

the effects of legislation enacted after the forecast was produced. Because the 

estimates of subsequently enacted legislation are themselves imperfect, the calculated 

7. The root mean square error is calculated by squaring the forecast errors, averaging those squares, 

and taking the square root of that average. Because the forecast errors in this case are measured as 

a percentage of the actual amount, the RMSE measurement reported in this assessment is often 

referred to as the root mean square percentage error. Likewise, the mean absolute error reported in 

this assessment is often referred to as the mean absolute percentage error.
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forecast errors are also imperfect; that is, what may appear to be misestimates in the 

baseline projections may in reality be misestimates of the effects of legislation, and vice 

versa. Those estimates of legislative effects exhibit error in part because they use only 

information available at the time of enactment and thus do not incorporate information 

that is available later, such as that from tax returns or the actual data on GDP or other 

macroeconomic indicators. Furthermore, even when all such information is on hand, 

analysts cannot calculate all of the actual effects on revenues of past legislative 

changes with certainty.

The conclusions that can be drawn from this forecast evaluation are also limited by 

the fact that the number of forecasts available is relatively small for the purpose of 

statistical analysis. Thirty or so years of forecasts is a small sample size given the 

variability of economic performance and the many different factors in the economy 

that interact with one another to determine the amount of revenues collected. 

Assessment of the Forecasts 

CBO’s projections for shorter time periods have tended to be more accurate than those 

for periods extending further into the future, through the six-year horizon—the longest 

projection period considered in this discussion. The same is true for the Administration’s 

revenue projections. In addition, CBO has been slow to incorporate new information 

into its revenue projections, resulting in revisions to estimates in one direction in a 

forecast tending to be followed by revisions in the same direction in the subsequent 

forecast.

Two-Year Forecasts

CBO’s two-year forecasts of total revenues cover the upcoming fiscal year—the year 

for which policymakers are typically considering appropriation bills and in which 

proposed changes to tax policy and mandatory spending would often first take effect. 

On average, CBO’s two-year revenue projections were overestimated—that is, actual 

revenues fell short of the projected amounts—by 1.1 percent between 1983 and 2014; 

an error of that magnitude would correspond to $37 billion out of the roughly 

$3.5 trillion in total revenues that CBO projected for 2016 (the two-year forecast) in its 

January 2015 baseline projections.8

 As with all measures cited, that mean error 

percentage is adjusted to exclude the effects of legislation enacted after the forecast 

was produced because the baseline is created under the assumption that no future 

legislation affecting revenues will be enacted.9

8. See Congressional Budget Office, The Budget and Economic Outlook: 2015 to 2025 (January 2015), 

www.cbo.gov/publication/49892.

9. Without excluding those effects of legislation, CBO’s average overestimate at the two-year horizon 

would have been about three times as large, indicating that policymakers have cut taxes more than 

they have raised them over the past 30 years or so (sometimes by extending previous cuts that were 

scheduled to expire).
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That relatively small average error of 1.1 percent does not capture the substantial 

amount of offsetting that occurs between the overestimates and underestimates. Only 

6 of the 32 two-year forecasts—4 overestimates and 2 underestimates—had errors of 

1.1 percent or less (with the sign of the error disregarded). Overall, exactly half of the 

forecasts were overestimated and half were underestimated, but the overestimates, 

most notably for years that ended up being marked by recession, tended to be larger. 

The forecast error of the revenue projection for 2009, which was released in January 

2008, just about the time the recession hit, is the largest for all of the two-year 

forecasts, at 24.9 percent. Indeed, if that one forecast is excluded from the analysis, the 

average error at the two-year horizon is an overestimate of 0.3 percent rather than 

1.1 percent.

The RMSE of CBO’s two-year forecasts is 7.4 percent, and the mean absolute error is 

5.2 percent, both much larger than the mean error of 1.1 percent, which for such a 

limited number of forecasts suggests the lack of statistical bias. Such a result is 

consistent with the past findings of several researchers who have studied CBO’s 

revenue forecasts.10 Testing for statistical bias is not straightforward for multistep 

forecasts such as these, in which the overlap of time periods in consecutive forecasts 

causes errors to be related over time. In this case, the fact that both the RMSE and 

mean absolute error are significantly larger than the mean error indicates a significant 

degree of offsetting from overestimates and underestimates, making it more likely that 

a small average error in one direction is simply the result of random chance. One 

implication is that the addition of more forecasts in a future analysis could well 

eliminate the mean error or even swing it in the opposite direction. 

In addition to exhibiting little or no statistical bias, CBO’s two-year forecasts have tended 

to misestimate revenues by relatively small amounts more often than by larger amounts. 

Together, those characteristics imply that the RMSE would provide a useful guide for 

assessing the distribution of forecast errors. In fact, three-quarters of the two-year 

forecasts—24 of the 32—had errors between -7.4 percent and 7.4 percent, an interval 

spanning plus or minus one RMSE. That fraction is not far from the approximately twothirds of errors that would fall within that range if the errors were normally distributed 

around zero. About half of the two-year forecasts had errors between about -3 percent 

and 3 percent.

CBO’s two-year forecasts do not appear to have become more accurate over time—

indeed, the largest forecast errors have been in the more recent years—but that may 

reflect particular hard-to-predict events in the most recent period, rather than a 

10. See Alan J. Auerbach, “On the Performance and Use of Government Revenue Forecasts,” National 

Tax Journal, vol. 52, no. 4 (December 1999), pp. 767–782, http://tinyurl.com/pqxq8an; Rudolph 

G. Penner, “Federal Revenue Forecasting,” in Penner, Handbook of Government Budget Forecasting

(Taylor & Francis, 2008); and George A. Plesko, “The Accuracy of Government Forecasts and 

Budget Projections,” National Tax Journal, vol. 41, no. 4 (December 1988), pp. 483–501, 

http://tinyurl.com/olcmrue.
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deterioration in CBO’s forecasting accuracy outside of those events. In particular, the 

largest errors occurred following the 2001 recession and, to an even greater extent, 

the 2007–2009 recession. The latter was the largest economic downturn since the Great 

Depression, and the two largest sustained declines in the stock market over the historical 

period occurred during those recessions. Even apart from those events, the small sample 

of forecasts makes it difficult to draw conclusions about changes in the accuracy of 

CBO’s forecasts over time.

Over the past few decades, the accuracy of the Administration’s revenue forecasts was 

very similar to that of CBO’s forecasts. By CBO’s calculation, forecast errors for the 

Administration’s two-year revenue projections averaged 1.7 percent, just slightly higher 

than CBO’s. In addition, the RMSE and mean absolute error of the Administration’s 

two-year forecasts—7.8 percent and 5.5 percent, respectively—are both very similar to 

those of CBO’s forecasts. 

Six-Year and Other Forecasts 

On average, CBO overestimated revenues in its six-year forecasts for 1987 to 2014 

(those published between 1982 and 2009) by 5.3 percent (see Figure 4). The error 

jumped for the last few of those forecasts as a result of the unexpected 2007–2009 

recession. If the forecasts for 2009 to 2014 are excluded—the overestimates for those 

years averaged 17.5 percent—CBO still, on average, overestimated revenues in its 

six-year forecasts, but by a much smaller 2.0 percent. 

As with the mean error of the two-year forecasts, the mean error of the six-year forecasts 

includes a substantial amount of offsetting between underestimates and overestimates; 

unlike the case with forecasts for the shorter period, however, overestimates more clearly 

dominate those for the longer horizon. In total, about 19 forecasts overestimated 

revenues and 9 underestimated them. For 14 of the 19 forecasts with overestimates, a 

recession occurred in the intervening years that affected the amount of revenues 

collected in the sixth year. About two-thirds of the forecasts—11 overestimates and 

7 underestimates—have errors of 13 percent or less, but only about one-third have 

errors of about 5 percent or less. Of the 8 overestimates that exceed 13 percent, 

5 (those for 2009 to 2013) were affected by the 2007–2009 recession. The forecast 

error of the revenue projection for 2012, which was produced in January 2007, is 

23 percent—the largest error of all of CBO’s six-year projections. 

The mean errors of CBO’s forecasts are progressively larger for each additional year 

of the horizon. On average, CBO’s one-year projections—those for the year in 

progress at the time the forecast was produced—underestimated revenues by only 

0.1 percent. However, the average overestimate of 1.1 percent at the two-year horizon 

grows to 3.7 percent at the four-year horizon, 4.7 percent at the five-year horizon, and 

5.3 percent at the six-year horizon.
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 13

The RMSE and mean absolute errors also tend to be larger for longer time horizons, 

although there is some evidence of a leveling off of the errors at the end of the six-year 

period. The RMSE of CBO’s one-year revenue projections is 3.0 percent; it is 

7.4 percent for CBO’s two-year projections and 10.0 percent for the three-year 

projections. Although the RMSE continues to grow with each additional year of the 

projection period through the six-year horizon, it does so at a slower rate, reaching 

11.1 percent for the four-year projections, 11.9 percent for the five-year projections, 

and 12.1 percent for the six-year projections. A similar pattern exists for the mean 

absolute error.

The RMSE is not as useful a guide to the distribution of the misestimates of the sixyear projections as it is for the two-year projections. That is because the six-year 

projections have a larger mean error than the two-year projections and have 

misestimated revenues by larger amounts about as often as by smaller amounts: Just 

over half of the six-year projections have had forecast errors in either direction of less 

than 12.1 percent, an interval spanning plus or minus one RMSE. 

The accuracy of CBO’s and the Administration’s forecasts, as measured by the mean 

error and RMSE, was very similar. The Administration’s six-year projections, for 

example, have a mean error of 6.6 percent and an RMSE of 13.4 percent, by CBO’s 

calculation, which are similar to the mean error and RMSE of CBO’s six-year 

projections (5.3 percent and 12.1 percent, respectively). Taking a slightly different 

approach, CBO calculates that the Administration’s projection errors are also similar 

when the Administration’s forecasts are adjusted using the estimates of the revenue 

effects of major pieces of subsequently enacted tax legislation that were published by 

the Treasury Department (in a working paper by one of its economists) rather than 

CBO’s estimates.11 The RMSE of the Administration’s six-year projections measures 

13.0 percent calculated using those alternative estimates, slightly lower than the 

13.4 percent measured using CBO’s estimates for the same legislation.12

Efficient Use of New Information

One characteristic of CBO’s forecast record that has proven difficult to correct is that 

the agency has tended to revise consecutive revenue forecasts in the same direction. 

That tendency suggests that CBO may not be efficiently incorporating all new 

11. See Jerry Tempalski, “Revenue Effects of Major Tax Bills, Updated Tables for All 2010 Tax Bills” 

(supplemental material from June 2011 for Revenue Effects of Major Tax Bills, Working Paper 81, 

Office of Tax Analysis, revised September 2006), http://go.usa.gov/cCuEP (PDF, 93 KB). The 

legislative estimates are provided for total revenues, not for the components of revenues. Because 

the estimated effects are not provided beyond the five-year horizon, CBO extrapolated the estimates 

for the sixth year.

12. In many cases CBO does not have available its own estimates for the separate pieces of enacted 

legislation, just the sum of effects of legislation enacted between baselines. Therefore, for the 

purpose of comparison the agency used the Treasury Department’s estimate of the revenue effects of 

a major piece of enacted legislation as if it was the only legislation enacted between two baselines.
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 14

information into its forecasts. Forecasts produced in the mid- to late 1980s, in the early 

2000s, and between 2007 and 2009 tended to overestimate revenues for shorter 

horizons and were subject to repeated downward revisions, whereas those from the 

mid- to late 1990s and from the mid-2000s—periods of economic booms—tended to 

underestimate revenues and were repeatedly revised upward. To some degree, those 

forecast errors were related to the business cycle, but even after shifts between 

expansion and recession in the business cycle became apparent, it still took time for 

CBO to incorporate that information and the related effects on revenues into its 

forecasts. 

The correlation between revisions to CBO’s revenue forecasts was first quantified by 

Alan Auerbach in 1999 when he analyzed forecasts that the agency made between 

1985 and 1999.13 In the present analysis, CBO found that the correlation of its 

forecast revisions was smaller over the past 15 years than in the time period that was 

initially assessed by Auerbach. However, the limited number of forecasts that can be 

assessed makes it difficult to draw conclusions about any improvement that CBO may 

have made in incorporating information into its projections over time.

The correlation of forecast revisions has varied a lot over the past 30 years, suggesting 

that adjusting for the average correlation over the entire period would have improved 

accuracy for some forecasts and worsened it for others. CBO’s overall forecast errors, 

as measured by the RMSE, would not have been systematically improved if the agency 

had adjusted its forecasts to incorporate what was known about the pattern of its past 

forecast revisions at the time each forecast was made; that method would have over 

adjusted the forecasts in many cases. Despite the lack of a systematic improvement in 

forecast accuracy from such an approach, CBO continually assesses newly available 

information for the components of its economic and revenue forecasts to try to ensure 

that it is using the latest information in the most efficient manner.

Some Sources of Forecast Error

Forecast error in baseline revenue projections can arise from myriad sources. The 

forecast of GDP, the key input into the revenue projections, can miss short-term 

movements in the business cycle, longer-term trends in productivity growth, and other 

important developments that affect revenues. A variety of other unexpected 

developments can contribute to errors in projections of revenues as a percentage of 

GDP—and therefore in projections of revenues—including changes in the composition 

of GDP and national income; the relationship between national income and the 

associated tax bases; the relative growth rates of asset prices and GDP; the distribution 

of income among taxpayers; and the effects of recent changes in policies. 

13. See Alan J. Auerbach, “On the Performance and Use of Government Revenue Forecasts,” National 

Tax Journal, vol. 52, no. 4 (December 1999), pp. 767–782, http://tinyurl.com/pqxq8an. 
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 15

The forecast errors for GDP have tended to increase progressively as the horizon 

lengthens, through six years, resulting from misestimates in both cyclical factors and 

noncyclical factors such as productivity growth. Forecast errors for revenues as a 

percentage of GDP have tended to increase through the four-year horizon but then to 

decline slightly for five- and six-year projections. Misestimates of revenues as a 

percentage of GDP have tended to increase or decrease along with misestimates of 

GDP, especially at turning points in the business cycle, when both tend to be larger. Of 

the forecasts of particular sources of revenues, those of corporate income taxes have 

exhibited the largest errors (in percentage terms) and those of payroll taxes the smallest.

It is clear from the historical record that forecasts completed around business cycle 

peaks have had the largest errors in forecasting revenues. For example, the two-year 

forecasts that CBO released in early 1982, 1990, 2001, and 2008—near the 

beginning of recessions—significantly overestimated the amount of revenues that were 

collected in 1983, 1991, 2002, and 2009. (Lags between economic activity and the 

payment of taxes caused the largest shortfalls in revenues to occur some period after 

the beginning of the recessions.) The overestimates of revenues for those four forecasts 

range from 9 percent to 25 percent, yielding an RMSE of 16.8 percent, whereas the 

RMSE for all of CBO’s other two-year forecasts is 4.8 percent (see Figure 5). The 

Administration’s forecasts have exhibited very similar errors.

Errors Related to the Size of the Economy 

CBO’s revenue projections are critically affected by the projections of the overall size of 

the economy, typically measured by GDP and national income. Both of those measures 

include such variables as wages and salaries, corporate profits, and proprietors’ 

income. 

CBO regularly assesses its economic forecasts to look for ways in which they might be 

improved.14 The following key insights about forecast errors have been gleaned from 

those assessments:

 Forecast errors tend to be large around business cycle peaks, just before a recession 

begins. In some cases, the errors occur because the recessions are prompted by 

shocks that were unforeseeable at the time the forecasts were made; in others, the 

recessions occur during periods of especially great economic uncertainty, so widely 

different forecasts appeared equally probable.

 Productivity trends can change in unexpected ways. Shifts in capital accumulation and 

the effects of specific policies on incentives to invest, both important determinants of 

productivity growth, can be difficult to identify until several years after the fact.

14. For the latest analysis, see Congressional Budget Office, CBO’s Economic Forecasting Record: 

2015 Update (February 2015), www.cbo.gov/publication/49891.
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 16

 Oil prices fluctuate widely for reasons that are very difficult to predict. Sometimes 

those fluctuations affect the accuracy of overall inflation projections.

 Historical data are subject to revision, making it difficult to discern when forecast 

errors stem from imperfect data and when they stem from imperfect forecasting 

approaches.

Errors Attributable to Other Factors

In addition to the size of the overall economy, an array of other factors affect the 

amount of revenues collected per dollar of GDP. Those other factors appear to have 

been more significant sources of forecast error for shorter time horizons, but the size of 

the economy appears to have been at least as important as those factors for longer 

horizons through six years. 

Those errors in projecting the revenue yield on GDP (that is, revenues measured as a 

percentage of GDP) arise in part because the individual components of national 

income generated at a given level of GDP can be difficult to predict. The mix of 

different types of income, as measured in the national income and product accounts 

compiled by the Bureau of Economic Analysis (BEA), is very important to revenue 

forecasts because each category of income is taxed at a different effective rate. For 

example, wages and salaries, which are subject to both the individual income tax and 

payroll taxes, generate more revenues per dollar of income than do other types of 

income included in the macroeconomic forecast. Corporate profits, which are taxed 

at the next highest rates, vary more from year to year than other income sources and 

thus are especially difficult to forecast accurately. Some sources of income included 

in the macroeconomic forecast, such as interest income, are effectively taxed at much 

lower rates than other income because a much larger share of those sources is 

earned in forms that are not subject to taxation. 

In addition, income reported on tax returns, which is generally projected to grow at the 

rate of the comparable measure in the national income and product accounts, may 

actually grow at a different rate. For example, the growth rate of wages and salaries 

reported on tax returns may be different from that of wages measured in national 

income because of coverage or sampling differences. Similarly, taxable profits reported 

on corporate tax returns may grow at a different rate than the profits recorded in the 

national income accounts: BEA calculates profits by subtracting the losses of 

unprofitable businesses from the profits of profitable ones, but unprofitable businesses 

do not typically pay corporate income taxes.

Even if the amounts of income reported on tax returns were predicted correctly on the 

basis of their corresponding categories in the national income accounts, other factors 

that are difficult to project would still affect the amount of revenues collected. One such 

factor is the distribution among individual taxpayers of a specific component of 

income. For instance, wages and salaries may accrue more or less than expected to 
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 17

high-income taxpayers, who face the highest tax rates. Also, because the national 

income accounts measure income earned in the current period, errors in projecting 

components of the tax base that correspond to income from prior periods, such as 

pension distributions and capital gains realizations, contribute to errors in projecting 

the revenue yield for a given level of GDP. In recessions, the decline in the revenue yield 

on GDP contributed to significant revenue shortfalls, when certain categories of taxable 

income, such as wages and salaries, corporate profits, and capital gains realizations, 

unexpectedly declined relative to GDP and the highest-earning taxpayers unexpectedly 

earned a smaller share of overall income, pushing down effective tax rates.

Errors Attributable to Misestimates of the Size of the Economy Versus Other Factors

To examine the relative importance of the size of the economy versus other factors, 

CBO compared errors in forecasts of GDP with those in projections of revenues as a 

share of GDP.15 By that analysis, misestimates of revenues as a percentage of GDP are 

larger than misestimates of GDP for shorter horizons, but the misestimates of the two 

measures are closer together for longer horizons:

 The RMSE of CBO’s two-year projections of GDP is 2.5 percent; for projections of 

revenues as a share of GDP, it is 5.4 percent (see Table 3).16

 At the four-year projection horizon, the forecast errors of each of the two factors 

are higher than those at the two-year horizon; the RMSE of forecasts of revenues 

as a percentage of GDP (7.1 percent) exceeds the RMSE of forecasts of GDP 

(4.8 percent). 

 At the six-year projection horizon, CBO’s misestimates of GDP (which have an 

RMSE of 7.0 percent) and of revenues as a share of GDP (which have an RMSE of 

6.4 percent) are very similar. The RMSE of projections of GDP at the six-year horizon 

is higher than that of projections at the four-year horizon, continuing the pattern of 

errors increasing with the length of the horizon; the RMSE of projections of revenues 

as a percentage of GDP, however, was slightly lower at the six-year than at the 

four-year horizon.17

Because the measure revenues as a share of GDP is itself a percentage, the forecast 

errors associated with it can also be expressed in percentage points (that is, as the 

15. To account for the effect of revisions to the historical amounts of GDP, CBO used the projected 

growth rate of GDP and applied it to current estimates of the historical amounts of GDP to calculate 

errors in past projections of GDP. The historical amounts of revenues have been revised by only 

small amounts; therefore, the revenue projections have not been adjusted in the same way.

16. The RMSEs for GDP and for revenues as a share of GDP do not sum to the corresponding RMSE for 

total revenues because of interactions between the two and the squaring of the errors. 

17. The same basic pattern emerges if only the forecasts through January 2009 are included and if the 

more recent forecasts, for which actual results are known only for the shorter horizons, are excluded 

so that the same number of forecasts is analyzed for each horizon. 
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 18

difference between the projected and actual percentages of GDP). CBO’s six-year 

forecasts of revenues as a share of GDP (which averaged 17.2 percent of GDP over 

the years covered in this report) have an RMSE of 1.1 percentage points and a mean 

absolute error of 0.9 percentage points. In CBO’s January 2015 baseline projections, 

revenues for 2020, the sixth year of the projection, total 18.0 percent of GDP.18 On the 

basis of the mean absolute error of past forecasts, revenues for that year might be 

expected to be as low as 17.1 percent of GDP or as high as 18.9 percent if there are 

no changes to current law. (The actual error for that particular projection might still fall 

outside that range.)

The pattern of errors in CBO’s revenue forecasts growing with the length of the horizon 

has presumably occurred because noncyclical factors that affect the projections of 

potential (or maximum sustainable) GDP and actual GDP, such as long-term 

productivity growth, have tended to steadily boost errors in projections of GDP and 

revenues. It is not surprising that the magnitude of errors in projections of productivity 

increases for longer horizons because each year’s productivity growth rate is uncertain 

and misestimates of productivity would compound over time.19

By contrast, errors for projections of GDP that are related to the business cycle should 

not continue to grow after the first few years of a particular projection’s horizon have 

passed because, in CBO’s estimation, the size of the economy would be expected to 

return close to its potential after a few years.20 If potential GDP was projected correctly 

for those later years, then the forecast errors for GDP would relate to where the 

economy was in its cycle—overestimates and underestimates would occur, but they 

would not increase in magnitude for longer forecast horizons unless the nature of the 

cyclical upturns and downturns changed. 

Likewise, cyclically related errors in projections of revenues as a share of GDP should 

not continue to grow after the first few years of a projection’s horizon have passed. That 

pattern is discernable in CBO’s historical forecasting record: The RMSE of CBO’s 

projections for revenues as a percentage of GDP reaches a peak at the four-year 

horizon and declines slightly at the five- and six-year horizons. 

It is unclear whether errors for CBO’s revenue projections extending beyond six years—

either the small set that the agency has produced in recent years or those that it will 

produce in the near future—will generally continue to increase beyond those observed 

at the six-year horizon or if they will instead stabilize or even decline. Indeed, the 

18. See Congressional Budget Office, The Budget and Economic Outlook: 2015 to 2025

(January 2015), www.cbo.gov/publication/49892.

19. See Alan J. Auerbach, “Fiscal Uncertainty and How to Deal With It,” Working Paper 6, 

Hutchins Center on Fiscal and Monetary Policy, Brookings Institution (December 15, 2014), 

http://tinyurl.com/knuldte. 

20. Because of the nature and depth of the 2007–2009 recession, CBO‘s projections in recent years 

have had GDP return toward its potential level more slowly than has occurred historically.
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 19

accuracy of CBO’s current revenue projections could be quite different from the 

average accuracy of those projections over the agency’s history.

Interactions Between Misestimates of GDP and Other Factors 

In CBO’s forecasting record, misestimates of GDP have tended to move together 

with misestimates of revenues as a percentage of GDP, especially at turning points 

in the business cycle (see Figure 6). In particular, misestimates of revenues as a 

percentage of GDP have been more cyclical than misestimates of GDP. Given that 

GDP affects revenues more than revenues affect GDP, it appears that misestimates of 

GDP can be used to predict misestimates of other factors that determine the amount 

of revenues. As a result, attributing the errors separately is an oversimplification 

because improved (or worsened) accuracy in projecting GDP, particularly during 

turning points, would probably lead in general to improved (or worsened) accuracy in 

projecting revenues as a percentage of GDP. However, the relationship between 

accuracy in projecting those measures is complicated and not consistent across time 

periods.

The relationship between misestimates of GDP and those of other revenue-determining 

factors appears strongest during business cycle downturns, but it is also evident during 

cyclical booms. During recessions, CBO has overestimated not only GDP but also key 

components of the tax base relative to GDP, such as capital gains realizations, pension 

distributions, and corporate profits. The overestimates of those components, 

furthermore, have often exceeded, in percentage terms, the overestimate of GDP. 

Misestimates of GDP and misestimates of revenues as a percentage of GDP have 

exhibited a similar relationship even when the economy was not experiencing a cyclical 

downturn. For example, in the late 1990s, an unexpected cyclical boom resulted in 

CBO’s underestimating GDP and, to a greater extent, revenues as a percentage of GDP.

Errors in Projections of Different Sources of Federal Revenues

The amount of uncertainty about factors affecting revenues varies substantially by 

revenue source. Of CBO’s two-year revenue projections, those of corporate income 

taxes have the highest RMSE—29 percent—and those of payroll taxes, the lowest—

3.5 percent (see Figure 7). The RMSE of projections of corporate receipts is enlarged 

by a few very sizable errors; the median absolute error is much smaller—11 percent—

and the mean absolute error is 18 percent.21

CBO’s projections of individual income tax receipts, the largest source of federal 

revenues, have an RMSE of 10.2 percent, which falls in the middle of the error rates for 

the tax sources. The projections of customs duties and of excise taxes have RMSEs of 

8.5 percent and 8.8 percent, respectively. And the projections of miscellaneous receipts 

21. The median absolute error is the error that on the basis of magnitude falls in the middle of all errors 

(when the direction of the errors is ignored). Half of the errors are larger than the median absolute 

error, and half are smaller.
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 20

(fees and fines and Federal Reserve earnings) and of estate and gift taxes have RMSEs of 

15.7 percent and 18.0 percent. 

For each revenue source, CBO would attribute about the same percentage error to 

misestimates of GDP, and misestimates of revenues as a percentage of GDP would 

account for the remaining, generally much larger, part of the total projection error.22

For example, the RMSE of CBO’s two-year projections for GDP was 3 percent, and that 

for its forecasts of individual income taxes as a percentage of GDP was 8 percent—

yielding a total RMSE (including interactions) of 10.2 percent. Correspondingly, for 

corporate income taxes, the RMSE for GDP of 3 percent combines with that for 

revenues as a percentage of GDP of 26 percent to yield the total RMSE of 29 percent. 

For payroll taxes, however, misestimates of GDP and of revenues as a percentage of 

GDP account for more equal portions of the total RMSE.

The divergence in forecasting outcomes by revenue source is largely explained by the 

fact that the tax bases for the different revenue sources have varying degrees of 

predictability. Most notably, the primary tax base for payroll taxes—wages and 

salaries—tends to increase at more stable rates over time than most other tax bases, 

making it more predictable. Corporate profits, which fluctuate significantly over the 

business cycle, are notoriously difficult to project accurately. It is therefore not surprising 

that projections of payroll taxes have been the most accurate and projections of 

corporate income taxes the least accurate. The tax base for individual income taxes is 

made up primarily of wages and salaries, but it also includes much more variable 

sources of income, such as business income taxed at the individual level and capital 

gains realizations, which fluctuate significantly with the prices of assets and with 

taxpayers’ decisions about when to sell their assets.

The projections of the smaller sources of revenues, which combined have averaged 

about the size of receipts from corporate income taxes, depend on factors related to 

more limited parts of the economy that can vary widely and in unexpected ways. For 

example, estate and gift taxes are assessed on the wealth of large estates, which 

can fluctuate widely with asset prices. In addition, miscellaneous fees and fines can 

vary substantially for industry-specific reasons. 

The projections for specific tax sources that contribute the most to the errors in 

projections of total revenues are not necessarily those with the largest errors. A 

relatively small error in a projection of a large source of federal revenues can result in a 

large error in the projection of total revenues, just as a large deviation in a projection 

of a small source of revenues can result in a small total forecast error. 

22. Misestimates in projecting GDP could affect different revenue sources in different ways, but the 

effects would be broadly similar among revenue sources. For purposes of this analysis, CBO simplified the calculations by attributing the same percentage error in estimates of GDP to each source so 

that each 1 percentage-point misestimate of GDP would yield a 1 percentage-point misestimate for 

each source of revenue. 
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 21

To determine which source-specific projections have contributed the most to the errors in 

the projections for total revenues, CBO calculated errors for projections of each tax 

source relative to total revenues, rather than to revenues from that source alone.23 By that 

metric, projections of revenues from the largest source, individual income taxes, with an 

RMSE relative to total revenues of 4.6 percent, have contributed the most to the forecast 

errors of CBO’s two-year projections of total revenues (see Table 4). The projections 

for the third largest source, corporate income taxes, have an RMSE relative to total 

revenues of 2.4 percent, larger than that for the second largest, payroll tax receipts 

(1.3 percent), even though corporate income tax receipts have averaged less than onethird of the amount of payroll tax receipts over the past few decades. The variability of 

corporate income taxes over the business cycle magnifies their importance in determining 

total forecast accuracy. The projections of smaller tax sources have contributed much 

smaller amounts to forecast errors for total revenues; the RMSE for each of those sources 

relative to total revenues is 0.6 percent or less.

Another pattern that emerges in the data is that the forecast errors for the largest 

revenue sources have tended to move together. The errors in projections of individual 

income taxes and payroll taxes have a correlation coefficient of 0.75, indicating a high 

degree of correlation.24 For those two sources of revenues, wages and salaries are a 

major part of the tax base, so the correlation of the errors in their projections is not a 

surprise. The correlation coefficient of projection errors for individual income taxes and 

corporate income taxes is smaller at 0.62, but it still suggests a significant degree of 

correlation. Errors in projecting the overall size of the economy tend to cause errors in 

projections of individual and corporate incomes in the same direction, and errors in 

CBO’s projections of individual and corporate income taxes have moved together to 

some degree. However, misestimates of the shares of total income in the economy that 

accrue to labor and capital tend to cause forecast errors for individual and corporate 

income taxes to be negatively correlated—but that effect has not been predominant in 

CBO’s forecasting record. The correlation coefficient of errors in projections of payroll 

taxes and corporate income taxes is smaller still at 0.39, indicating a weaker positive 

relationship.

As with the projections of total revenues, the accuracy of the Administration’s forecasts 

of the revenue components has been very similar to CBO’s (see Figure 7). For their 

projections of both individual income taxes and payroll taxes, CBO’s and the 

Administration’s RMSEs are virtually the same. For corporate income taxes, the RMSE of 

23. The RMSE of projections for a single tax source relative to total revenues can be approximated by 

multiplying the RMSE of the projections for a source by that source’s average share of total revenues 

over the period. However, the more that a revenue source’s share of total revenues varies over the 

period, the less precise that approximation becomes. The estimates provided use the precise 

calculation.

24. The correlation coefficient varies between 1, indicating that the two measures move perfectly in 

tandem, and -1, indicating that the two measures move completely opposite each other.
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 22

the Administration’s projections is 32 percent and that of CBO’s projections is 

29 percent. Likewise, for the smaller sources of revenues, the RMSEs of the 

Administration’s projections generally have been a little higher than those for CBO’s 

projections.

The revenue projections made by state governments from 1987 to 2013 had a degree 

of accuracy for comparable sources of revenues very similar to CBO’s over the slightly 

longer span examined here.25 For the two-year forecasts by state governments, 

projections of corporate income taxes have larger errors (a median absolute error of 

11.8 percent) than projections of individual income taxes (which have a median 

absolute error of 4.4 percent). (The corresponding figures for CBO’s projections are 

about 11 percent and 6 percent.)26 One factor complicating a comparison is that most 

states produce their forecasts slightly closer to the start of the subsequent fiscal year 

than does CBO. 

In addition to income taxes, states also derive a substantial share of revenues from 

sales taxes, which, like payroll taxes at the federal level, are much more predictable 

than the other revenue sources. Not only do the federal and state revenue forecasts 

share similarities in the size of errors for projections of particular revenue sources, they 

also share the characteristic that the errors are especially large for those projections 

that are made around the time of recessions.

25. See Donald J. Boyd and Lucy Dadayan, “State Tax Revenue Forecasting Accuracy: Technical Report,” 

The Nelson A. Rockefeller Institute of Government and the State University of New York (September 

2014), http://tinyurl.com/pxocc94 (PDF, 1.25 MB).

26. Boyd and Dadayan’s assessment of state forecasts, cited above, includes only median errors for 

those sources of revenues, so CBO cannot compare its mean errors.
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 23

About This Document

Each year, typically in January or February, the Congressional Budget Office issues a 

report on the state of the budget and the economy. This document provides 

background information on the accuracy of the revenue projections included in those 

past reports. In keeping with CBO’s mandate to provide objective, impartial analysis, 

this report makes no recommendations.

Mark Booth of CBO’s Tax Analysis Division and Logan Timmerhoff (formerly of CBO) 

wrote the report with guidance from David Weiner. Nathaniel Frentz, Jeffrey Holland, 

Peter Huether, and Christopher Williams of CBO and Douglas Elmendorf (formerly of 

CBO) provided helpful comments. Alan Auerbach of the University of California at 

Berkeley, Dean Croushore of the University of Richmond, and Alec Phillips of Goldman 

Sachs also reviewed the report. The assistance of external reviewers implies no 

responsibility for the final product, which rests solely with CBO.

Jeffrey Kling, John Skeen, and Robert Sunshine reviewed the report, Bo Peery edited it, 

and Maureen Costantino and Jeanine Rees prepared it for publication. This report is 

available on CBO’s website (www.cbo.gov/publication/50831).

Keith Hall 

Director

November 2015
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 24

Figure 1. Return to Reference

Forecast Errors for CBO’s and the Administration’s Two-Year Revenue Projections

Percent

Sources: Congressional Budget Office; Office of Management and Budget.

Notes: The years shown are those for which each forecast was made; CBO’s two-year projections were typically published in January or 

February of the previous year. For example, the value of 11.9 percent for CBO’s forecast error for 1983 indicates that the projection 

published in February 1982 for revenues in fiscal year 1983 overestimated revenues by that amount.

Forecast errors are projected amounts minus actual amounts, expressed as a percentage of actual amounts.

Table 1. Return to Reference

Summary Measures of the Accuracy of CBO’s Revenue Projections

Percent

Source: Congressional Budget Office.

Notes: The revenue projections included here are those published between 1982 and 2014, typically in January or February. A one-year 

projection is for the fiscal year in which it is published, a two-year projection is for the fiscal year beginning eight to nine months after 

it is published, a three-year projection is for the fiscal year starting one year and eight to nine months after it is published, and so on.

Forecast errors are projected amounts minus actual amounts, expressed as a percentage of actual amounts; thus, a positive error

indicates an overestimate.

The mean error is the arithmetic average of the forecast errors. The mean absolute error is the average of forecast errors without 

regard to direction (the negative signs are removed from underestimates before averaging). The root mean square error is calculated 

by squaring the forecast errors, averaging those squares, and taking the square root of the average.

1983 1988 1993 1998 2003 2008 2013

-10

0

10

20

30

CBO

Administration

CBO’s Mean Forecast Error,

1983 to 2014

(1.1%)

Mean Error -0.1 1.1 2.4 3.7 4.7 5.3

Mean Absolute Error 2.3 5.2 7.6 9.3 10.1 10.4

Root Mean Square Error 3.0 7.4 10.0 11.1 11.9 12.1

One-Year Two-Year Three-Year Four-Year Five-Year Six-Year
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 25

Figure 2. Return to Reference

A Timeline for Measuring the Forecast Errors of CBO’s Revenue Projections

Source: Congressional Budget Office.

Note: Actual dates for the one- and two-year projections for revenues that were published in 2013 (the most recent two-year projection 

included in this study) are provided here as an example. The timelines for measuring forecast errors are similar for all one- and 

two-year projections. 

Fiscal Year 2013

(Year 1)

Oct. Dec. Feb. Apr. June Aug.

CBO released baseline budget projections

for 2013 to 2023, which include one-year 

revenue projections for 2013, two-year 

projections for 2014, and so on. 

Fiscal Year 2014

(Year 2)

Oct. Dec. Feb. Apr. June Aug.

In October 2013, the Treasury Department 

released the actual revenue amounts for fiscal 

year 2013 (CBO’s one-year projection period).

Fiscal Year 2015

(Year 3)

Oct. Dec. Feb. Apr. June Aug.

In October 2014, the Treasury Department 

released the actual revenue amounts for fiscal 

year 2014 (CBO’s two-year projection period).
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 26

Table 2. Return to Reference

CBO’s 2009 Revenue Projections Adjusted to Account for Estimates of 

Subsequently Enacted Legislation

Billions of Nominal Dollars

Source: Congressional Budget Office.

Notes: Actual data for the revenue projections that were published in January 2009 (including the most recent six-year projection for which 

actual amounts are available) are provided here as an example. Adjustments to account for the effects of subsequently enacted 

legislation (such as those shown in the table) allow for a comparison between projected and actual amounts.

The periods identified for the legislation correspond to the intervals between successive baseline projections, which CBO typically 

publishes in January or February and updates in August of each year.

The estimated effects on revenues of specific pieces of legislation were generally provided by the staff of the Joint Committee on 

Taxation near the time of the legislation’s enactment.

The three laws that were enacted in the five years after CBO’s January 2009 baseline projection that had the largest effects on

revenues were the American Recovery and Reinvestment Act of 2009 (enacted in February 2009); the Tax Relief, Unemployment 

Insurance Reauthorization, and Job Creation Act of 2010 (enacted in December 2010); and the American Taxpayer Relief Act of 2012 

(enacted in January 2013).

* = between -$500 million and $500 million.

January 2009 Baseline Projection of Revenues 2,357 2,533 2,825 3,124 3,353 3,544

Estimated Effects of Subsequent Legislation Enacted in Each Period

January 2009 to August 2009 -61 -166 2 8 -1 25

August 2009 to January 2010 0 -44 4 7 8 26

January 2010 to August 2010 -9 -2 6 31 74

August 2010 to January 2011 0 -409 -335 -68 16

January 2011 to August 2011 * * -3 -2

August 2011 to January 2012 0 -20 -9 -1

January 2012 to August 2012 -70 -21 -37

August 2012 to February 2013 0 -280 -299

February 2013 to May 2013 0 0

May 2013 to February 2014 0 *

February 2014 to August 2014 1

August 2014 to September 2014 0 ____ ____ ____ ____ ____ ____

Total -61 -219 -405 -405 -343 -198

Adjusted January 2009 Baseline Projection of Revenues 2,296 2,315 2,420 2,720 3,010 3,346

Fiscal Year

(Year 6)

2014

(Year 5)

2013

(Year 4)

2011 2012

(Year 2) (Year 3)

2009 2010

(Year 1)
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 27

Figure 3. Return to Reference

CBO’s Two-Year Revenue Projections Compared With Actual Amounts

Billions of Nominal Dollars

Sources: Congressional Budget Office; Office of Management and Budget.

Notes: The years shown are those for which each forecast was made; the two-year projections were typically published in January or February 

of the previous year. For example, in February 1982, CBO projected that revenues for 1983, the first year shown, would total 

$672 billion, but the actual amount of revenues for the year was $601 billion. Thus, the projection exceeded the actual amount by 

$71 billion.

The baseline projections were adjusted to incorporate the estimated effects of legislation enacted after their publication, generally on 

the basis of the estimated effects of that legislation provided by the staff of the Joint Committee on Taxation near the time of the 

legislation’s enactment.

1983 1988 1993 1998 2003 2008 2013

-400

-200

0

200

400

600

0

1,000

2,000

3,000

4,000

CBO’s Projections

Difference Between

Projected and

Actual Amounts

Actual Amounts
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 28

Figure 4. Return to Reference

Forecast Errors for CBO’s and the Administration’s Six-Year Revenue Projections

Percent

Sources: Congressional Budget Office; Office of Management and Budget.

Notes: The years shown are those for which each forecast was made. CBO’s six-year projections were published five years before the date 

shown, typically in January or February (that is, four years and eight or nine months before the indicated fiscal year began). For 

example, the value of 16.1 percent for CBO’s forecast error for 1987 indicates that the projection published in February 1982 for 

revenues in fiscal year 1987 overestimated revenues by that amount.

Forecast errors are projected amounts minus actual amounts, expressed as a percentage of actual amounts.

Figure 5. Return to Reference

Root Mean Square Errors of CBO’s and the Administration’s Two-Year Revenue Projections 

Produced Near Business Cycle Peaks Compared With Those Produced at Other Times

Percent

Sources: Congressional Budget Office; Office of Management and Budget.

Notes: This analysis covers projections published between 1982 and 2013 for fiscal years 1983 to 2014.

The projections produced near business cycle peaks include those published near the beginning of calendar years 1982, 1990, 2001, 

and 2008 for fiscal years 1983, 1991, 2002, and 2009. The projection published in February 1982, which significantly overestimated 

revenues for 1983, is included, although the previous business cycle peak occurred in July 1981.

1987 1992 1997 2002 2007 2012

-20

-10

0

10

20

30

40

CBO's Mean Forecast Error,

1987 to 2014

(5.3%)

CBO

Administration

Projections Produced Near Business Cycle Peaks Other Forecasts All Forecasts

0

5

10

15

20

CBO Administration
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 29

Table 3. Return to Reference

Errors in CBO’s Revenue Projections Attributable to Misestimates of GDP and Other Factors

Percent

Source: Congressional Budget Office.

Notes: CBO typically publishes revenue projections in January or February of each year. A two-year projection is for the fiscal year beginning 

eight to nine months after it is published, a four-year projection is for the fiscal year beginning two years and eight to nine months 

after it is published, and the six-year projection is for the fiscal year beginning four years and eight to nine months after it is published. 

This analysis covers the two-year projections published between 1982 and 2013, the four-year projections published between 1982

and 2011, and the six-year projections published between 1982 and 2009.

Forecast errors are projected amounts minus actual amounts, expressed as a percentage of actual amounts; thus, a positive error

indicates an overestimate.

The mean error is the arithmetic average of the forecast errors. The mean absolute error is the average of forecast errors without 

regard to direction (the negative signs are removed from underestimates before averaging). The root mean square error (RMSE) is

calculated by squaring the forecast errors, averaging those squares, and taking the square root of the average.

The errors for projections of GDP and for projections of revenues as a percentage of GDP do not add up to the corresponding errors for 

projections of total revenues because of interactions between the two and, in the case of RMSEs, also because of the squaring of the 

errors.

GDP =gross domestic product; * = between zero and 0.05 percent.

a. Projections of revenues as a percentage of GDP reflect the factors other than misestimates of GDP that contribute to forecast errors, such 

as the composition of national income among different types of labor and capital income and the distribution of different types of income 

among higher- and lower-income taxpayers.

b. This measure is simply the difference between the projection for revenues as a percentage of GDP and the actual percentage of GDP, 

whereas the standard forecast error for projections of revenues as a percentage of GDP is that difference divided by the actual percentage 

of GDP. 

GDP

Mean Error * 1.7 3.5

Mean Absolute Error 1.9 3.7 5.7

Root Mean Square Error 2.5 4.8 7.0

Revenues as a Percentage of GDPa

Mean Error 0.9 1.7 1.6

Mean Absolute Error 3.9 6.0 5.3

Root Mean Square Error 5.4 7.1 6.4

Total Revenues

Mean Error 1.1 3.7 5.3

Mean Absolute Error 5.2 9.3 10.4

Root Mean Square Error 7.4 11.1 12.1

Memorandum:

Revenues as a Percentage of GDP (Percentage points)b

Mean Error 0.1 0.2 0.2

Mean Absolute Error 0.7 1.0 0.9

Root Mean Square Error 0.9 1.2 1.1

Two-Year Four-Year Six-Year
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 30

Figure 6. Return to Reference

Forecast Errors for CBO’s Two-Year Projections of GDP and of Revenues as a Percentage of GDP

Percent

Source: Congressional Budget Office.

Notes: The years shown are those for which each forecast was made; CBO’s two-year projections were typically published in January or 

February of the previous year. For example, the values of 6.3 percent for the forecast error of CBO’s projection of GDP in 1983 and of 

5.2 percent for the forecast error of its projection of revenues as a percentage of GDP in that year indicate that the projections 

published in February 1982 for fiscal year 1983 overestimated GDP and revenues as a percentage of GDP by those amounts.

Forecast errors are projected amounts minus actual amounts, expressed as a percentage of actual amounts.

GDP = gross domestic product.

Figure 7. Return to Reference 1, 2

Root Mean Square Errors of CBO’s and the Administration’s Two-Year Projections, 

by Source of Revenues

Percent

Sources: Congressional Budget Office; Office of Management and Budget.

Notes: This analysis covers projections published between 1982 and 2013 for fiscal years 1983 to 2014.

Forecast errors are projected amounts minus actual amounts, expressed as a percentage of actual amounts. The root mean square 

error is calculated by squaring the forecast errors, averaging those squares, and taking the square root of the average.

“Miscellaneous Receipts” includes fees, fines, and remittances from the Federal Reserve System.

1983 1988 1993 1998 2003 2008 2013

-10

-5

0

5

10

15

20

Revenues as a

Percentage of

GDP

GDP

Individual

Income Taxes

Payroll Taxes Corporate

Income Taxes

Excise Taxes Customs Duties Estate and Gift

Taxes

Miscellaneous

Receipts

Total Revenues

0

10

20

30

40

CBO

Administration
CBO

CBO’S REVENUE FORECASTING RECORD NOVEMBER 2015 31

Table 4. Return to Reference

Root Mean Square Errors of CBO’s Two-Year Projections, by Source of Revenues

Percent

Source: Congressional Budget Office.

Notes: This analysis covers projections published between 1982 and 2013 for fiscal years 1983 to 2014.

Errors for projections of a specific tax source are projected amounts for that source minus actual amounts, expressed as a percentage 

of the actual revenues from that source. Errors for projections of a specific tax source relative to total revenues are projected amounts 

for that source minus actual amounts, expressed as a percentage of actual total revenues. The latter measure allows the errors of 

projections for each source to be compared with one another.

The root mean square error (RMSE) is calculated by squaring the forecast errors, averaging those squares, and taking the square root 

of the average.

The RMSE of projections for a single tax source relative to total revenues can be approximated by multiplying the RMSE of the 

projections for a source by that source’s average share of total revenues over the period. However, the more that a revenue source’s 

share of total revenues varies over the period, the less precise that approximation becomes.

“Miscellaneous Receipts” includes fees, fines, and remittances from the Federal Reserve System.

n.a. = not applicable.

Individual Income Taxes 10.2 45.4 4.6

Payroll Taxes 3.5 36.1 1.3

Corporate Income Taxes 29.0 9.9 2.4

Excise Taxes 8.8 3.7 0.4

Customs Duties 8.5 1.3 0.1

Estate and Gift Taxes 18.0 1.1 0.2

Miscellaneous Receipts 15.7 2.4 0.6 _____

Total Revenues 7.4 100.0 n.a.

Root Mean Square Error Average Share of Total Revenues

Root Mean Square Error,

Relative to Total Revenues