CBO’s Economic 

Forecasting Record: 

2019 Update

OCTOBER 2019

CONGRESS OF THE UNITED STATES

CONGRESSIONAL BUDGET OFFICE

-4

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0

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1976–

1977

1981–

1982

1986–

1987

1991–

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1996–

1997

2001–

2002

2006–

2007

2011–

2012

2016–

2017

Errors in Two-Year Forecasts of Real Output Growth

Percentage Points

Administration

Blue Chip Consensus

CBO
At a Glance

In this report, the Congressional Budget Office assesses its two-year and 

five-year economic forecasts and compares them with forecasts of the 

Administration and the Blue Chip consensus. 

Measures of Quality. CBO focuses on three measures of forecast quality—

mean error, root mean square error, and two-thirds spread of errors—that help 

the agency identify the centeredness (that is, the opposite of statistical bias), 

accuracy, and dispersion of its forecast errors.

The Quality of CBO’s Forecasts. CBO’s forecasts of most economic variables 

are, on average, too high by small amounts. As measured by the root mean 

square error, its two-year forecasts of most variables are not appreciably more 

accurate than its five-year forecasts. CBO is least accurate in forecasting growth 

of wages and salaries.

Comparison With Other Forecasts. For the most part, CBO’s and the 

Administration’s forecasts exhibit similar degrees of centeredness, but CBO’s 

forecasts are slightly more accurate and have smaller two-thirds spreads. For 

all three quality measures, CBO’s forecasts are roughly comparable to the 

Blue Chip consensus forecasts.

Sources of Forecast Errors. All forecasters failed to anticipate certain key 

economic developments, resulting in significant forecast errors. The main 

sources of those errors are turning points in the business cycle, changes in labor 

productivity trends and crude oil prices, the persistent decline in interest rates, 

the decline in labor income as a share of gross domestic product, and data 

revisions. 

Forecast Uncertainty. CBO uses its past forecast errors to gauge the uncertainty of its current forecasts. For example, using the root mean square error, 

CBO estimated in August 2019 that there is approximately a two-thirds 

chance that economic growth will average between 0.7 percent and 3.3 percent 

over the next five years. Its central estimate is 2.0 percent.

www.cbo.gov/publication/55505
Contents

Summary 1

How CBO Measures Forecast Quality 1

The Quality of CBO’s Forecasts 1

Comparing CBO’s Forecasts With the Administration’s Forecasts 

and the Blue Chip Consensus Forecasts 1

Sources of Forecast Errors 3

How CBO Uses Its Forecast Errors to Estimate Uncertainty 4

CBO’s Methods for Evaluating Forecasts 4

Selecting Forecast Variables 5

Calculating Forecast Errors 5

BOX 1. HOW CBO CALCULATES ECONOMIC FORECAST ERRORS 6

Measuring Forecast Quality 7

Limitations of the Forecast Evaluations 8

Differences From CBO’s 2017 Report 8

The Quality of CBO’s Forecasts 9

A Comparison of Forecast Quality 9

Real and Nominal Output Growth 9

BOX 2. COMPARING CBO’S AND THE FEDERAL RESERVE’S TWO-YEAR FORECASTS 10

Unemployment Rate 11

Inflation 11

Interest Rates 11

Wages and Salaries 11

Some Sources of Forecast Errors 11

Turning Points in the Business Cycle 11

Changes in Labor Productivity Trends 13

Changes in Crude Oil Prices 15

The Persistent Decline in Interest Rates 15

The Decline in the Labor Share 18

Data Revisions 18

Quantifying the Uncertainty of CBO’s Economic Forecasts 20

Appendix: The Data CBO Uses to Evaluate Its Economic Forecasting Record 25

List of Tables and Figures 28

About This Document 29

CBO’s Economic Forecasting Record: 2019 Update

Summary

Each year, the Congressional Budget Office prepares 

economic forecasts that underlie its projections of the 

federal budget. CBO forecasts hundreds of economic 

variables, but some—including output growth, the 

unemployment rate, inflation, interest rates, and wages 

and salaries—play a particularly significant role in the 

agency’s budget projections. To evaluate the quality of 

its economic projections, estimate uncertainty ranges, 

and isolate the effect of economic errors on budgetary 

projections, CBO regularly analyzes its historical forecast errors. That analysis serves as a tool for assessing the 

usefulness of the agency’s projections. 

In this report, CBO evaluates its two-year and five-year 

economic forecasts from as early as 1976 and compares 

them with analogous forecasts from the Administration 

and the Blue Chip consensus—an average of about 

50 private-sector forecasts published in Blue Chip 

Economic Indicators. External comparisons help identify 

areas in which the agency has tended to make larger 

errors than other analysts. They also indicate the extent 

to which imperfect information may have caused all 

forecasters to miss patterns or turning points in the 

economy.

How CBO Measures Forecast Quality

CBO’s analysis focuses on three metrics of forecast 

quality—mean error, root mean square error, and twothirds spread of errors:

• The mean error is CBO’s primary measure of 

centeredness, which indicates how close the average 

forecast value is to the average actual value over 

time. Centeredness is the opposite of statistical bias, 

which quantifies the degree to which a forecaster’s 

projections are too high or too low over a period of 

time. 

• The root mean square error is CBO’s primary 

measure of accuracy, or the degree to which forecast 

values are dispersed around actual outcomes. 

• The two-thirds spread, computed as the range 

between the minimum and maximum errors after 

removing the one-sixth largest errors and the onesixth smallest errors, illustrates a forecaster’s typical 

range of errors and provides information about the 

extent of the dispersion of those errors.

The Quality of CBO’s Forecasts

When a mean error is used to assess the quality of a forecast, the standard for comparison is a mean error of zero. 

A forecast that has a mean error of zero is, on average, 

neither too high nor too low. CBO’s forecasts of most 

economic variables tend to exhibit small positive mean 

errors—that is, on average, they are too high by small 

amounts.1 CBO’s forecasts of interest rates and growth 

in wages and salaries exhibit larger mean errors than its 

forecasts of other economic indicators.

When a root mean square error and spread are used to 

assess the quality of a forecast, there is no absolute standard for comparison. Moreover, it is difficult to compare 

quality measures across variables because magnitudes of 

variables can differ substantially, and some variables are 

relatively easy to forecast but others are relatively hard. 

It is possible, however, to compare those measures across 

forecast horizons for a given variable. For example, as 

measured by the root mean square error, CBO’s twoyear forecasts of most variables are not appreciably more 

accurate than its five-year forecasts. For most variables, 

the agency’s two- and five-year forecasts exhibit a similar 

two-thirds spread of errors. 

Comparing CBO’s Forecasts With the 

Administration’s Forecasts and the Blue Chip

Consensus Forecasts

In general, forecasts produced by CBO, the 

Administration, and the Blue Chip consensus display 

similar error patterns over time. Because all forecasters 

faced the same challenges, periods in which CBO made 

1. Forecast errors throughout this report were calculated as 

projected values minus actual values; thus, a positive error is an 

overestimate. 
2 CBO’s Economic Forecasting Record: 2019 Update October 2019

Table 1.

Summary Measures for Two-Year and Five-Year Forecasts, by Sample Years

Percentage Points

Two-Year Forecasts Five-Year Forecasts

CBO Administration

Blue Chip 

Consensus CBO Administration

Blue Chip 

Consensus

Output

Growth of real outputa 1980–2017b 1979–2014

Mean error -0.1 0.2 0 0.2 0.5 0.1

Root mean square error 1.3 1.4 1.3 1.2 1.3 1.1

Two-thirds spread 1.9 2.2 2.0 2.0 2.4 1.9

Growth of nominal output 1980–2017 1982–2014

Mean error 0.2 0.5 0.4 0.6 0.8 0.7

Root mean square error 1.5 1.6 1.5 1.2 1.4 1.2

Two-thirds spread 2.4 3.0 2.4 2.0 2.2 1.6

Unemployment

Unemployment rate 1982–2017 1982–2014

Mean error 0.2 0.1 0.1 0 -0.1 0

Root mean square error 0.8 0.8 0.8 1.1 1.2 1.1

Two-thirds spread 1.3 1.1 1.4 1.7 2.0 1.7

Inflation

Inflation in the consumer price index 1981–2017 1983–2014

Mean error 0.2 0.2 0.3 0.2 0.1 0.4

Root mean square error 0.9 0.9 0.9 0.6 0.6 0.8

Two-thirds spread 1.5 1.7 1.6 0.9 1.2 1.1

Inflation differentialc 1981–2017 1983–2014

Mean error -0.1 -0.2 -0.1 -0.1 -0.2 -0.2

Root mean square error 0.4 0.5 0.4 0.4 0.5 0.4

Two-thirds spread 0.7 0.9 0.7 0.8 1.0 0.8

Interest Rates

Interest rate on 3-month Treasury bills 1981–2017 1983–2014

Mean error 0.5 0.2 0.5 1.2 0.8 1.3

Root mean square error 1.2 1.3 1.2 1.7 1.6 1.7

Two-thirds spread 2.3 2.1 1.9 2.5 3.2 2.5

Real interest rate on 3-month Treasury billsd 1981–2017 1983–2014

Mean error 0.3 0.1 0.2 0.9 0.7 0.9

Root mean square error 1.2 1.4 1.3 1.6 1.6 1.5

Two-thirds spread 2.2 2.2 2.1 2.7 3.2 2.7

Interest rate on 10-year Treasury notes 1984–2017 1984–2014

Mean error 0.4 0.2 0.5 0.8 0.4 1.0

Root mean square error 0.8 0.9 0.8 1.1 1.3 1.2

Two-thirds spread 1.1 1.9 1.2 1.0 2.4 1.2

Continued
October 2019 CBO’s Economic Forecasting Record: 2019 Update 3

large overestimates typically coincide with periods in 

which other forecasters made similarly large overestimates. Over time, however, even small differences in the 

magnitude of errors can result in appreciable differences 

in measures of forecast quality.

CBO and the Blue Chip consensus tend to produce 

less-biased forecasts of output growth but more-biased 

forecasts of interest rates than the Administration (see 

Table 1 on page 2). As measured by the root mean 

square error, CBO’s forecasts tend to be slightly more 

accurate, on net, than the Administration’s forecasts and 

roughly comparable to the Blue Chip consensus forecasts. 

Finally, CBO’s forecasts tend to exhibit smaller twothirds spreads than the Administration’s forecasts do; the 

agency’s forecasts tend to have two-thirds spreads that are 

similar to those of the Blue Chip consensus forecasts. 

Sources of Forecast Errors

Forecasters made large errors in their economic forecasts 

as a result of several economic developments: 

• Turning points in the business cycle; 

• Changes in labor productivity trends; 

• Changes in crude oil prices; 

• The persistent decline in interest rates;

• The decline in the labor share—that is, labor income 

as a share of gross domestic product (GDP); and 

• Data revisions. 

Table 1. Continued

Summary Measures for Two-Year and Five-Year Forecasts, by Sample Years

Percentage Points

Two-Year Forecasts Five-Year Forecasts

CBO Administration

Blue Chip

Consensus CBO Administration

Blue Chip

Consensus

Wages and Salaries

Growth of wages and salaries 1980–2017 1980–2014

Mean error 0.4 0.7 n.a. 1.0 1.1 n.a.

Root mean square error 1.8 2.0 n.a. 1.8 1.9 n.a.

Two-thirds spread 2.9 2.9 n.a. 2.1 2.7 n.a.

Change in wages and salaries as a share of output 1980–2017 1980–2014

Mean error 0.1 0.1 n.a. 0.1 0.1 n.a.

Root mean square error 0.4 0.5 n.a. 0.3 0.3 n.a.

Two-thirds spread 0.8 0.9 n.a. 0.6 0.6 n.a.

Sources: Congressional Budget Office; Office of Management and Budget; Wolters Kluwer, Blue Chip Economic Indicators; Bureau of Economic 

Analysis; Bureau of Labor Statistics; Federal Reserve.

Forecast errors are calculated by subtracting the average actual value from the average projected value over the two- and five-year time horizon.

For details on the data underlying the summary measures presented here, see the appendix.

CPI = consumer price index; n.a. = not available.

a. The measure of output is gross national product in years before 1992 and gross domestic product in 1992 and following years.

b. The quality comparisons for each variable shown use the sample of data that is common to all three forecasters. Because forecasts by the 

Administration and the Blue Chip consensus are not available for the full period of CBO’s forecasts, the samples generally are shorter than CBO’s full 

sample dating back to 1976.

c. The inflation differential is the difference between growth in the CPI and growth in the output price index.

d. The real interest rate is the nominal interest rate deflated by projected growth in CPI inflation.
4 CBO’s Economic Forecasting Record: 2019 Update October 2019

Some of those developments resulted in errors in forecasting specific variables. Changes in crude oil prices, 

for example, resulted in misestimates of inflation in the 

consumer price index (CPI). Other developments, such 

as turning points in the business cycle, affect the entirety 

of an economic forecast, and their effects are observable 

in the error patterns of several variables.

How CBO Uses Its Forecast Errors to 

Estimate Uncertainty

CBO most frequently uses the root mean square error of 

its historical forecasts to quantify the uncertainty of its 

current economic projections. For example, CBO’s baseline forecast for real (inflation-adjusted) GDP growth 

over the next five years is 2.0 percent. Using its historical 

root mean square error for that variable (1.3 percentage 

points), CBO then estimates that there is approximately 

a two-thirds chance that the rate of real GDP growth 

over the next five years will be between 0.7 percent and 

3.3 percent. Although the agency typically uses the 

historical root mean square error to estimate uncertainty, 

it uses the two-thirds spread of errors in instances where 

the statistical bias of its historical forecasts is high.

CBO’s Methods for Evaluating Forecasts

To evaluate the quality of its forecasts, CBO examines 

its historical forecast errors and compares them with 

errors made by the Administration and the Blue Chip

consensus. Comparison with the Blue Chip consensus is 

particularly useful because it incorporates a wide variety of viewpoints and methods, and some research has 

suggested that composite forecasts often provide better 

estimates than projections made by a single forecaster.2

2. See Allan Timmermann, “Forecast Combinations,” in Graham 

Elliott, Clive W. J. Granger, and Allan Timmermann, eds., 

Handbook of Economic Forecasting, vol. 1 (North Holland, 2006), 

pp. 135–196, https://doi.org/10.1016/S1574-0706(05)01004-9; 

Andy Bauer and others, “Forecast Evaluation With CrossSectional Data: The Blue Chip Surveys,” Economic Review, 

vol. 88, no. 2 (Federal Reserve Bank of Atlanta, 2003), 

pp. 17–31, https://tinyurl.com/y3qmyjzg (PDF, 219 KB); Henry 

Townsend, “A Comparison of Several Consensus Forecasts,” 

Business Economics, vol. 31, no. 1 (January 1996), pp. 53–55, 

www.jstor.org/stable/23487509; and Robert T. Clemen, 

“Combining Forecasts: A Review and Annotated Bibliography,” 

International Journal of Forecasting, vol. 5, no. 4 (1989), 

pp. 559–583, https://doi.org/10.1016/0169-2070(89)90012-5. 

For this analysis, CBO reviewed the economic projections that it published each winter, usually in January, 

beginning in 1976, as the basis for its baseline budget 

projections. Each of those economic projections spans 

the current year (that is, the calendar year already under 

way) and either 5 or 10 subsequent years. This report 

evaluates CBO’s economic forecasts over the first two 

years and first five years of its baseline projection period. 

CBO evaluates forecasts over the two-year time horizon 

because that interval is most relevant when the agency 

is preparing its baseline budget projections for the 

upcoming fiscal year. (That second year is often called 

the budget year.) CBO evaluates forecasts over the fiveyear time horizon to better understand the quality of its 

longer-term projections.3 The agency calculates errors 

by subtracting the average actual value from the average 

projected value.

The span of years evaluated for this analysis varies by 

economic indicator and depends on two factors: the 

availability of historical forecast data and the availability 

of actual economic data. To ensure that differences in the 

availability of forecast data do not affect the comparisons 

of forecast errors, those comparisons begin in the earliest 

year for which forecast data were available for all three 

sets of forecasts. So although CBO has two-year real 

output growth forecasts dating back to 1976, its forecast 

errors for comparison purposes are computed starting in 

1980—the first year the Blue Chip consensus data were 

available.4 Likewise, the final year of forecast analysis 

depends on the availability of actual economic data. This 

report incorporates data through the end of 2018, which 

allows CBO to analyze two-year forecasts that were made 

through the beginning of 2017 and five-year forecasts 

that were made through the beginning of 2014. (See the 

appendix for details.)

3. CBO cannot compare forecasts over longer time horizons because 

some data are not available. Although the agency has produced 

11-year economic forecasts since 1992, it produced only 6-year 

economic forecasts before then. In addition, the Blue Chip

consensus currently produces long-run economic forecasts 

spanning just 7 years.

4. The time periods used in the comparison of forecast quality vary 

on the basis of the availability of comparable data. Table 1 shows 

the sample periods used for each comparison.
October 2019 CBO’s Economic Forecasting Record: 2019 Update 5

Selecting Forecast Variables

CBO examines 10 forecast variables on the basis of their 

relative importance in the economic outlook and their 

relevance to projections of revenues, outlays, and deficits. 

Those variables include measures of output growth, the 

unemployment rate, inflation, interest rates, and wages 

and salaries.

Projections of real and nominal output growth are 

fundamental to CBO’s budget projections. Faster output 

growth is typically accompanied by faster growth in real 

income and hence faster growth of revenues from individual income taxes. Similarly, periods of faster output 

growth are typically associated with smaller transfer 

payments and smaller expenditures on unemployment 

insurance, resulting in lower outlays.

New to this analysis is an examination of CBO’s errors 

in forecasting the unemployment rate, a key component 

of the agency’s economic forecast. CBO’s forecast of 

the unemployment rate affects its projections of inflation, interest rates, and other labor market variables and 

also informs the agency’s projections of certain outlays, 

including unemployment insurance outlays.

CBO’s evaluation of inflation forecasts focuses on 

two measures: the percentage change in the CPI and 

the inflation differential, which is computed as the 

difference between growth in the CPI and growth in 

the output price index.5 All else being equal, higher 

CPI inflation implies faster growth in federal outlays 

(because the index is used to adjust payments to Social 

Security beneficiaries as well as payments made for some 

other programs) and slower growth in federal revenues (because elements of the individual income tax, 

5. For most years examined here, the inflation forecasts were for 

the CPI-U, which measures inflation in the prices of goods 

and services consumed by all urban consumers. Some forecasts, 

however, were for the CPI-W, which measures inflation in the 

prices of goods and services consumed by urban wage earners and 

clerical workers. CBO forecast the CPI-W from 1976 to 1978 

and again from 1986 to 1989; the Administration forecast the 

CPI-W through 1991. For the purpose of this evaluation, the 

distinction between the two measures was most consequential 

in 1984, when inflation in the CPI-U and CPI-W diverged by 

0.9 percentage points.

including the tax brackets, are indexed to the CPI).6

Growth in the output price index is closely linked to 

growth in nominal income subject to federal taxes, which 

implies faster growth in revenues. Consequently, if CPI 

inflation grew more quickly than anticipated and the 

output price index grew more slowly than anticipated, 

the projected deficit would generally be larger than 

expected.

The interest rates on 3-month Treasury bills and 10-year 

Treasury notes summarize CBO’s projections of shortand long-term interest rates, respectively. Interest rates 

primarily affect the budget through their effect on net 

interest outlays—the difference between income earned 

on interest-bearing assets and the cost of servicing the 

debt. As a result, overestimates of interest rates result 

in overestimates of debt and deficits. This report also 

analyzes the real 3-month Treasury bill rate, which is 

computed by removing the effects of CPI inflation 

from forecasts of the nominal 3-month Treasury bill 

rate. Considering errors in projecting the real 3-month 

Treasury bill rate isolates interest rate errors from errors 

in inflation projections.

Finally, CBO examines growth in wage and salary 

disbursements and the change in those disbursements 

as a share of output. Wages and salaries are the largest 

component of national income, and their growth 

informs CBO’s revenue projections and its analysis of the 

distribution of income. Analyzing wages and salaries as 

a share of output offers an approximation of forecasters’ 

views about the labor share of national income and helps 

isolate errors in projecting wages and salaries from errors 

in projecting nominal output.

Calculating Forecast Errors

CBO computes each forecast error as the difference 

between the average forecast value and the average actual 

value. (See Box 1 for an example of how CBO calculates 

its forecast errors.) The actual values are reported as calendar year averages and are based on the latest available 

data from various agencies. A positive error indicates that 

the forecast value exceeded the actual value, whereas a 

6. In future analyses, tax brackets will be indexed to the chained CPI 

as a result of policy changes in the Tax Cuts and Jobs Act of 2017.
6 CBO’s Economic Forecasting Record: 2019 Update October 2019

Box 1.

How CBO Calculates Economic Forecast Errors

The Congressional Budget Office calculates forecast errors by 

subtracting the average actual value of an economic indicator over a two-year (or five-year) period from the average 

projected value of that indicator over the same period. For 

example, to calculate the error for the two-year forecast of 

the growth of real (inflation-adjusted) gross domestic product 

(GDP) that was published in the January 2000 Budget and Economic Outlook, CBO first calculated the geometric average of 

the projected growth rates of real GDP for calendar years 2000 

and 2001, which was 3.2 percent.1 The agency then calculated 

1. The geometric average is the appropriate measure for averaging growth 

rates. It is used to calculate the average for all indicators except the 

change in wages and salaries as a percentage of output. Because that 

the average actual growth rate of real GDP for those two 

years, which was 2.6 percent. Finally, it subtracted the average actual rate of 2.6 percent from the average projected rate 

of 3.2 percent, resulting in an error of 0.6 percentage points. 

To determine the error for the five-year forecast made that 

same year, CBO took the averages of projected and actual 

output growth rates for calendar years 2000 through 2004.

indicator is a ratio rather than a growth rate, the appropriate measure for 

averaging is the arithmetic average.

Example: Calculating the Error in the Two-Year Forecast of the Growth of Real GDP That CBO Published in January 2000

CBO’s 

Forecast

Rate

The error for the two-year 

forecast made in 2000 is . . .

Calculate the 

Two-Year 

Average

Actual

Rate

2000

2001

3.3%

3.1%

4.1%

1.0%

3.2% – 2.6% = 0.6 percentage points

Sources: Congressional Budget Office; Bureau of Economic Analysis.

GDP = gross domestic product.

negative error indicates that the forecast value was below 

the actual value.

The method used to calculate forecast errors for this report 

differs from the method used in CBO’s revenue and 

outlay forecast evaluations.7 In those reports, errors were 

7. See Congressional Budget Office, An Evaluation of CBO’s Past 

Deficit and Debt Projections (September 2019), www.cbo.gov/

publication/55234, An Evaluation of CBO’s Past Outlay Projections 

calculated for a single fiscal year. For example, the error in 

CBO’s two-year revenue projection for 2007 is the percentage difference between the actual amount of revenues 

received in fiscal year 2007 and the revenues projected 

(November 2017), www.cbo.gov/publication/53328, and CBO’s 

Revenue Forecasting Record (November 2015), www.cbo.gov/

publication/50831.
October 2019 CBO’s Economic Forecasting Record: 2019 Update 7

for that year in January 2006.8 In this report, errors are 

calculated over a span of either two or five calendar years.

Measuring Forecast Quality

This evaluation focuses on three metrics of forecast quality: mean error, root mean square error, and two-thirds 

spread of errors. Together, those measures help CBO 

identify the statistical bias, accuracy, and dispersion of its 

forecast errors. Other measures of forecast quality, such 

as whether forecasters optimally incorporate all relevant 

information when making their projections, are harder 

to assess.9

Mean Error. CBO primarily uses the mean error—the 

arithmetic average of forecast errors—to measure the 

statistical bias of each variable. The agency uses the 

statistical bias to determine whether its forecasts are 

systematically too high or too low relative to actual economic outcomes. CBO’s goal is to provide forecasts of 

economic indicators that lie in the middle of the distribution of possible outcomes.

8. In evaluating its revenue projections, CBO calculated errors 

as the percentage difference (rather than the simple difference 

used in this report) between the projected and actual values 

because revenues are expressed as dollar amounts. If the errors 

in revenue projections were measured as simple differences in 

dollar amounts, they would be difficult to compare over time. (A 

$5 billion error in 1992, for example, would be significantly larger 

than a $5 billion error in 2014.) The simple difference is more 

appropriate here because this report evaluates errors in forecasts of 

economic indicators that are expressed as rates or percentages—

growth rates, interest rates, and changes in wages and salaries 

as a percentage of output. Forecast errors in this report are thus 

percentage-point differences between forecast and actual values.

9. Several studies that examined how well CBO’s economic forecasts 

incorporate relevant information—a characteristic referred 

to as forecast efficiency—found that the agency’s forecasts are 

relatively efficient. See, for example, Robert Krol, “Forecast Bias 

of Government Agencies,” Cato Journal, vol. 34, no. 1 (Winter 

2014), pp. 99–112, https://tinyurl.com/y7cmapw3 (PDF, 

88 KB); Stephen M. Miller, “Forecasting Federal Budget Deficits: 

How Reliable Are US Congressional Budget Office Projections?” 

Applied Economics, vol. 23, no. 12 (December 1991), pp. 1789–

1799, http://doi.org/10.1080/00036849100000168; and 

Michael T. Belongia, “Are Economic Forecasts by Government 

Agencies Biased? Accurate?” Review, vol. 70, no. 6 (Federal 

Reserve Bank of St. Louis, November/December 1988), 

pp. 15–23, http://tinyurl.com/ychze7ah. Although statistical tests 

can identify sources of inefficiency in a forecast after the fact, they 

generally do not indicate how such information could be used to 

improve forecasts when they are being made. 

The mean error does not, however, provide a complete 

characterization of the quality of a forecast. Because positive and negative errors are added together to calculate 

the average, forecast underestimates and overestimates 

offset one another. A small mean error might indicate 

that all forecasts had small errors, but a small mean 

error can also result from large overestimates and large 

underestimates that mostly offset one another. CBO uses 

the mean error as its primary measure of statistical bias 

because it is widely used and easily interpretable.10

Root Mean Square Error. CBO’s primary measure of 

forecast accuracy, the root mean square error, is calculated by squaring the forecast errors, averaging those 

squares, and taking the square root of that average.11

10. Several analysts outside of CBO have used more elaborate 

techniques to test for bias in the agency’s forecasts. One such 

alternative approach to testing a forecast for bias is based on 

linear regression analysis of actual values compared with forecast 

values. For details of that method, see Jacob A. Mincer and 

Victor Zarnowitz, “The Evaluation of Economic Forecasts,” in 

Jacob A. Mincer, ed., Economic Forecasts and Expectations: Analysis 

of Forecasting Behavior and Performance (National Bureau of 

Economic Research, 1969), pp. 3–46, www.nber.org/chapters/

c1214. Studies that have used that method to evaluate 

CBO’s and the Administration’s short-term forecasts have 

not found statistically significant evidence of bias over short 

forecast horizons. See, for example, Robert Krol, “Forecast 

Bias of Government Agencies,” Cato Journal, vol. 34, no. 1 

(Winter 2014), pp. 99–112, https://tinyurl.com/y7cmapw3

(PDF, 88 KB); Graham Elliott and Allan Timmermann, 

“Economic Forecasting,” Journal of Economic Literature, vol. 46, 

no. 1 (March 2008), pp. 3–56, https://doi.org/10.1257/

jel.46.1.3; George A. Krause and James W. Douglas, 

“Institutional Design Versus Reputational Effects on Bureaucratic 

Performance: Evidence from U.S. Government Macroeconomic 

and Fiscal Projections,” Journal of Public Administration Research 

and Theory, vol. 15, no. 2 (April 2005), pp. 281–306, https://

doi.org/10.1093/jopart/mui038; and Michael T. Belongia, “Are 

Economic Forecasts by Government Agencies Biased? Accurate?” 

Review, vol. 70, no. 6 (Federal Reserve Bank of St. Louis, 

November/December 1988), pp. 15–23, http://tinyurl.com/

ychze7ah. For more elaborate studies of bias that included CBO’s 

forecasts among a sizable sample, see J. Kevin Corder, “Managing 

Uncertainty: The Bias and Efficiency of Federal Macroeconomic 

Forecasts,” Journal of Public Administration Research and 

Theory, vol. 15, no. 1 (January 2005), pp. 55–70, https://

doi.org/10.1093/jopart/mui003; and David Laster, Paul Bennett, 

and In Sun Geoum, “Rational Bias in Macroeconomic Forecasts,” 

Quarterly Journal of Economics, vol. 114, no. 1 (February 1999), 

pp. 293–318, https://doi.org/10.1162/003355399555918. 

11. The mean square forecast error is equal to the square of the 

bias in the errors plus the variance of the errors. The variance 

measures the average squared difference between the errors and 

the mean error.
8 CBO’s Economic Forecasting Record: 2019 Update October 2019

That calculation places greater weight on instances in 

which the forecast values deviate significantly from actual 

values. Unlike the computation of mean error, forecast 

underestimates and overestimates do not offset one 

another when computing the root mean square error. 

CBO also uses the root mean square error of its historical forecasts to quantify the uncertainty of its current 

economic projections. When CBO’s historical forecasts 

display low statistical bias and have an approximately 

normal distribution of errors, about two-thirds of actual 

values will be within a range of plus or minus one root 

mean square error of the forecasted values. Since 2017, 

CBO has used that method to quantify the uncertainty 

of its projections of real GDP.

Two-Thirds Spread. CBO uses the two-thirds spread of 

errors—defined as the difference between the minimum 

and maximum error after removing the one-sixth largest 

and one-sixth smallest errors—to measure the dispersion 

of its forecast errors. Larger two-thirds spreads imply 

greater variability in forecast errors, whereas smaller 

two-thirds spreads imply a narrower range of forecast 

misestimates.

In certain cases, CBO uses the two-thirds spread instead 

of the root mean square error to quantify the uncertainty 

of its current economic projections. The agency relies on 

the two-thirds spread when the ratio of the mean error 

to the root mean square error is greater than or equal to 

0.3. Above that threshold, the root mean square error 

reflects a substantial amount of bias in the estimates in 

addition to their variance. When that criterion is met, 

CBO estimates that about two-thirds of actual values 

will be within a range of plus or minus one-half of the 

two-thirds spread of the forecasted values.

Limitations of the Forecast Evaluations

CBO’s interpretation of forecast errors is somewhat 

limited for two reasons. First, forecast methodology continues to evolve. Over time, CBO and other forecasters 

have adjusted the procedures they use to develop economic forecasts in response to changes in the economy 

and advances in forecasting methods. Even when such 

adjustments improve the quality of forecasts, they make 

it difficult to draw inferences about the size and direction 

of future errors.

The second challenge is understanding the effects of 

different assumptions about future fiscal policy. CBO 

is required by statute to assume that future fiscal policy 

will generally reflect the provisions in current law, an 

approach that derives from the agency’s responsibility 

to provide a benchmark for lawmakers as they consider 

proposed legislative changes.12 When the Administration 

prepares its forecasts, however, it assumes that the 

fiscal policy in the President’s proposed budget will be 

adopted. The private forecasters included in the Blue 

Chip survey all make their own assumptions about fiscal 

policy, but the survey does not report them. 

Forecast errors may be affected by those different fiscal 

policy assumptions, especially when forecasts are made 

while policymakers are considering major legislative 

changes. In early 2009, for example, contributors to the 

Blue Chip consensus reported that they expected additional fiscal stimulus, which implied stronger output 

growth than would be expected under current law. By 

contrast, CBO’s growth projections were tempered by 

the requirement that its forecasts reflect current law. In 

February 2009, shortly after CBO’s forecast was published, lawmakers enacted the American Recovery and 

Reinvestment Act (Public Law 111-5). Similarly, in early 

2017, the Blue Chip consensus forecast for 2017 and 

2018 probably incorporated some anticipation of a tax 

cut, as well as other changes in fiscal policy that would 

boost output in those years. Those anticipated changes 

probably led the Blue Chip consensus economic forecast 

to be stronger than CBO’s in early 2017.13 Major tax 

legislation was enacted in December 2017.

Differences From CBO’s 2017 Report 

In addition to updating the forecasts and historical data 

cited in the 2017 edition of CBO’s Economic Forecasting 

Record, this report compares errors in forecasts of a new 

variable—the unemployment rate.14 To illustrate the 

connection between CBO’s forecast errors and its 

current economic projections, the report also discusses 

how CBO quantifies the uncertainty in its economic 

12. There are some exceptions to that rule. For details, see 

Congressional Budget Office, What Is a Current-Law Economic 

Baseline? (June 2005), www.cbo.gov/publication/16558.

13. Different assumptions about monetary policy can also make it 

difficult to compare CBO’s forecasts with other forecasts. CBO’s 

forecasts incorporate the assumption that monetary policy will 

reflect the economic conditions that the agency expects to prevail 

under the fiscal policy specified in current law.

14. See Congressional Budget Office, CBO’s Economic Forecasting 

Record: 2017 Update (October 2017), www.cbo.gov/

publication/53090.
October 2019 CBO’s Economic Forecasting Record: 2019 Update 9

projections. Supplementing that discussion, this report 

adds a new measure of forecast quality—the two-thirds 

spread of errors—that serves as a secondary measure of 

forecast uncertainty.

The Quality of CBO’s Forecasts

Although CBO’s forecasts of most economic variables 

exhibit small positive mean errors, there are notable differences between variables. In particular, CBO’s forecasts 

of interest rates and growth in wages and salaries exhibit 

larger mean errors than its forecasts of other economic 

indicators. That pattern is observable over both the twoand five-year time horizons.

Similarly, as measured by the root mean square error, 

CBO’s two-year forecasts of most variables are not appreciably more accurate than its five-year forecasts, because 

anticipating short-term fluctuations is often more 

difficult than identifying long-term trends. For example, 

the agency’s two-year forecasts of inflation as measured 

by the CPI are less accurate than its five-year forecasts of 

that variable. That pattern does not hold for all variables, 

however. For example, CBO’s two-year forecasts of 

interest rates (for both the 3-month Treasury bill and the 

10-year Treasury note) are more accurate than its fiveyear forecasts of that variable. For both time horizons, 

however, CBO is least accurate when forecasting growth 

in wages and salaries.

CBO’s forecasts also exhibit similar two-thirds spreads 

for most variables across the two- and five-year forecast horizons. But there are exceptions. For example, 

CBO’s five-year forecasts of the unemployment rate 

and the interest rate on 3-month Treasury bills have 

larger error spreads than its comparable two-year forecasts. Conversely, the agency’s forecasts of CPI inflation, 

growth of nominal output, and the growth in wages and 

salaries have smaller two-thirds spreads over the five-year 

horizon than over the two-year horizon.

A Comparison of Forecast Quality

CBO compares its economic forecasts with analogous 

forecasts produced by the Administration and the Blue 

Chip consensus. (For a comparison of CBO’s two-year 

forecasts with forecasts made by the Federal Reserve, 

see Box 2.) The agency examines projections of output 

growth, the unemployment rate, inflation, interest rates, 

and growth in wages and salaries. Each set of forecasts 

displays similar error patterns over time, but small 

differences in the magnitude of those errors lead to some 

appreciable differences in measures of forecast quality. 

In terms of mean errors, CBO’s two- and five-year economic forecasts are broadly similar to forecasts produced 

by the Administration and the Blue Chip consensus. 

However, CBO and the Blue Chip consensus have 

smaller mean errors when forecasting output growth, 

whereas the Administration produces the least upwardly 

biased forecasts of interest rates. In terms of root mean 

square errors, CBO’s forecasts are similar to the Blue 

Chip consensus forecasts and slightly more accurate than 

the Administration’s forecasts. Finally, CBO’s forecasts 

tend to display two-thirds spreads that are smaller than 

those of the Administration’s forecasts and similar to 

those of the Blue Chip consensus forecasts.15 There are 

exceptions, however, for some variables and forecast 

horizons (see Table 1 on page 2).

Real and Nominal Output Growth

Over both the two- and five-year horizons, CBO’s forecasts of real and nominal output growth display less bias, 

greater accuracy, and smaller two-thirds spreads than the 

Administration’s forecasts. 

Compared with the Blue Chip consensus forecasts, 

CBO’s forecasts are roughly comparable for the various 

quality measures. Although CBO’s nominal output 

forecasts display slightly less statistical bias than the Blue 

Chip consensus forecasts, its real output forecasts display slightly more bias. The Blue Chip consensus produces five-year forecasts of real output growth that have 

smaller two-thirds spreads than the corresponding CBO 

forecasts. 

15. This description of relative forecast quality is strictly qualitative. 

CBO also conducted a series of statistical tests to assess the 

differences in root mean square errors among forecasters. 

Compared with the Administration’s forecasts, CBO’s forecasts 

had root mean square errors for 5 of 20 variables examined in this 

report that were smaller by statistically significant amounts. The 

Administration’s forecasts were never more accurate than CBO’s 

forecasts by amounts that were statistically significant. Compared 

with the Blue Chip consensus forecasts, CBO’s forecasts had 

root mean square errors for 2 of 20 variables examined in this 

report that were smaller by statistically significant amounts. 

The Blue Chip consensus forecasts were more accurate than 

CBO’s forecasts for 1 of those variables by an amount that was 

statistically significant.
10 CBO’s Economic Forecasting Record: 2019 Update October 2019

Box 2.

Comparing CBO’s and the Federal Reserve’s Two-Year Forecasts

Like the Administration and the Blue Chip consensus, the Federal 

Reserve regularly produces economic forecasts that serve as a 

useful comparison when evaluating the quality of forecasts by the 

Congressional Budget Office. The scope of the Federal Reserve’s 

forecasts is limited: It does not publish forecasts of interest rates 

or growth in wages and salaries, nor does it publish any five-year 

forecasts. As a result, CBO did not include the Federal Reserve’s 

forecasts in the principal analysis for this report. However, the 

Federal Reserve does publish comparable forecasts of real output 

growth and consumer price inflation for a two-year time horizon.

CBO’s forecasts of real output growth are generally similar to 

the Federal Reserve’s forecasts (see the figure). For all measures of forecast quality, both CBO and the Federal Reserve 

produce forecasts of real output growth that are relatively 

unbiased and that display a similar degree of accuracy. CBO’s 

forecasts, however, have a smaller two-thirds spread of errors.

CBO’s and the Federal Reserve’s forecasts of consumer price 

inflation are also quite similar. Each set of forecasts has a 

similar degree of accuracy and comparable two-thirds spreads. 

However, CBO tends to produce forecasts of consumer price 

inflation that contain an upward bias, whereas the Federal 

Reserve tends to produce unbiased forecasts.

The Federal Reserve’s forecasts differ from CBO’s forecasts in two 

ways. First, the Federal Reserve’s forecasts include the effects 

of anticipated changes in fiscal policy, whereas CBO’s forecasts 

reflect the assumption that current laws governing fiscal policy will 

remain generally unchanged. Second, the Federal Reserve’s recent 

forecasts are modal—that is, they represent the single most likely 

outcome for the economy. By contrast, CBO’s forecasts represent 

the middle of the range of possible outcomes. In periods when the 

range of possible outcomes is highly skewed, the Federal Reserve’s 

forecasts will differ from CBO’s. 

Comparison of Two-Year Forecasts by CBO and the Federal Reserve

Percentage Points

Consumer Price Inflation

Real Output Growth

1976–1977 1981–1982 1986–1987 1991–1992 1996–1997 2001–2002 2006–2007 2011–2012 2016–2017

1976–1977 1981–1982 1986–1987 1991–1992 1996–1997 2001–2002 2006–2007 2011–2012 2016–2017

-6

-4

-2

0

2

4

-6

-4

-2

0

2

4

CBO Federal Reserve

Sources: Congressional Budget Office; Federal Reserve; Bureau of Economic Analysis.

The measure of real output is gross national product in years before 1992 and gross domestic product in 1992 and later years. Positive errors 

represent overestimates. The dots shown on the horizontal axis indicate that the forecast period overlapped a recession by six months or 

more. The years indicate the time span covered by each of the forecast errors shown in the figure. 

Most inflation errors are errors in forecasting the consumer price index for all urban consumers, but some are errors in forecasting the 

consumer price index for urban wage earners and clerical workers, and some are errors in forecasting personal consumption expenditures. 

For details on the underlying data, see the appendix.
October 2019 CBO’s Economic Forecasting Record: 2019 Update 11

Unemployment Rate

CBO, the Administration, and the Blue Chip consensus 

tend to produce similar forecasts of the unemployment 

rate, which results in similar measures of forecast quality. Over both the two- and five-year time horizons, all 

three sets of forecasts show little variation in their mean 

errors, root mean square errors, and two-thirds spreads. 

Although the three forecasters have slightly overestimated 

the unemployment rate over the two-year time horizon, 

they have produced unbiased or slightly downward 

biased estimates over the five-year time horizon. 

Inflation

CBO’s forecasts of CPI inflation are similar to those of 

the Administration and the Blue Chip consensus over 

the two-year time horizon. At the five-year time horizon, CBO’s inflation forecasts are less biased and more 

accurate than forecasts from the Blue Chip consensus. 

Likewise, CBO’s five-year inflation forecasts display a 

smaller two-thirds spread than the forecasts of both the 

Administration and the Blue Chip consensus. 

Compared with the Administration, CBO produces 

forecasts of the inflation differential that display less bias, 

greater accuracy, and smaller two-thirds spreads. CBO’s 

forecasts are comparable to the Blue Chip consensus 

forecasts. Over both time horizons, each set of forecasts 

has underestimated the inflation differential—the only 

variable in this analysis with a uniform and persistent 

downward bias.

Interest Rates

All forecasters have struggled to produce high-quality 

forecasts of real and nominal interest rates. 

At the two-year horizon, CBO and the Blue Chip consensus produce interest rate forecasts that have a positive 

statistical bias. The Administration’s interest rate forecasts 

display less bias but also less accuracy than the other 

forecasters because its forecasts tend to include large and 

partially offsetting overpredictions and underpredictions. Two-thirds-spread measures do not follow a clear 

pattern; CBO has a noticeably larger two-thirds spread 

in its forecast of the nominal interest rate on 3-month 

Treasury bills, but the Administration has the largest 

two-thirds spread in its forecast of the interest rate on 

10-year Treasury notes.

Over the five-year time horizon, a similar pattern 

holds. CBO produces interest rate forecasts that are 

considerably more biased than, but roughly as accurate 

as, the Administration’s forecasts. CBO’s forecasts are 

comparable to the Blue Chip consensus forecasts. The 

five-year horizon differs from the two-year horizon in 

one respect: The Administration produces forecasts with 

a much larger two-thirds spread than the forecasts of 

both CBO and the Blue Chip consensus.

Wages and Salaries

Over the two-year horizon, CBO’s forecasts of the 

growth in wages and salaries are less biased and more 

accurate than the Administration’s forecasts. (The Blue 

Chip consensus does not provide forecasts of the growth 

in wages and salaries.) Over the five-year time horizon, 

CBO’s forecasts are similar in bias and accuracy but 

have a considerably smaller two-thirds spread than the 

Administration’s forecasts. Over both time horizons, 

CBO’s and the Administration’s forecasts exhibit significant upward bias.16 Forecasts of wages and salaries as 

a share of output are nearly indistinguishable from one 

another. 

Some Sources of Forecast Errors

Forecast errors often occur in response to difficulties in 

anticipating significant economic developments. Such 

developments include turning points in the business 

cycle, changes in labor productivity trends, changes in 

crude oil prices, the persistent decline in interest rates, 

the declining labor share, and data revisions. Some of 

those developments are closely tied to errors in forecasting specific variables—for example, the changes in crude 

oil prices that resulted in misestimates of CPI inflation. 

Other developments, such as turning points in the 

business cycle, have wide-ranging effects on economic 

forecasts and affect the projections of many variables.

Turning Points in the Business Cycle

Business cycle peaks and troughs mark the beginning 

and end of recessions—periods of significant economic 

contraction. Five recessions occurred in the years covered 

by this analysis: those in 1980, 1981 to 1982, 1990 to 

16. CBO also examined its forecasts of real growth in wages and 

salaries, which inform its analysis of the distribution of taxable 

income. Examining errors in real wage and salary growth helps 

isolate errors in forecasting nominal wage and salary growth 

from errors in forecasting growth in consumer price inflation. 

Although those forecasts exhibited slightly less statistical bias than 

their nominal counterparts, the accuracy of the forecasts was 

similar. For that reason, CBO elected not to include those results 

in its primary summary tables.
12 CBO’s Economic Forecasting Record: 2019 Update October 2019

Figure 1.

Root Mean Square Errors of Two-Year Forecasts Made Near Business Cycle Peaks

Percentage Points

Real Interest Rate on 3-Month Treasury Bills Growth of Wages and Salaries

Unemployment Rate Interest Rate on 3-Month Treasury Bills

Real Output Growth Nominal Output Growth

Recessions Other Years Recessions Other Years

Recessions Other Years Recessions Other Years

Recessions Other Years Recessions Other Years

0

1

2

3

4

5

0

1

2

3

4

5

0

1

2

3

4

5

CBO

Blue Chip

Consensus

Administration

CBO

Administration

a

Sources: Congressional Budget Office; Office of Management and Budget; Wolters Kluwer, Blue Chip Economic Indicators; Bureau of Economic 

Analysis; Bureau of Labor Statistics; Federal Reserve.

The root mean square errors for recessions are based on forecasts made near business cycle peaks—those published in 1981, 1990, 2001, and 

2008. The root mean square errors for other years are based on all two-year forecasts made through 2017, except for the four made near business 

cycle peaks. 

The measure of output is gross national product in years before 1992 and gross domestic product in 1992 and later years. Real output is nominal 

output adjusted to remove the effects of inflation.

a. The real interest rate is the nominal interest rate deflated by the projected rate of growth in the consumer price index.
October 2019 CBO’s Economic Forecasting Record: 2019 Update 13

1991, 2001, and 2007 to 2009. Although the depth and 

duration of each recession differed, all contributed to 

forecast misestimates that were substantially larger than 

those made in nonrecession years (see Figure 1). 

Forecasters struggle to produce accurate forecasts around 

business cycle downturns for three main reasons. Because 

business cycle downturns are hard to predict, the first 

challenge is anticipating when one will occur. During 

periods of growth, it is often difficult to identify which 

economic imbalances will ultimately result in a recession. 

Moreover, it is often difficult to know whether the economy is in a recession until well after it has begun. Thus, 

forecasts made just before a recession tend to be overly 

optimistic about economic outcomes.

The second challenge is identifying the length and severity of a recession. Recessions often coincide with periods 

of great uncertainty, both in economic outcomes and 

in fiscal and monetary policy. Under those conditions, 

a wide range of outcomes can appear equally probable, 

making it difficult to produce a forecast that is in the 

middle of the distribution of possible outcomes.

The final challenge is predicting the speed with which 

the economy will recover from a recession.17 Until the 

early 1990s, the U.S. economy typically grew rapidly 

for several quarters after a recession ended. Since then, 

however, recoveries have been much slower.18 Failing to 

predict slower economic recoveries has caused forecasters 

to overestimate economic growth in the aftermath of 

business cycle downturns.

Changes in Labor Productivity Trends

Growth of labor productivity in the nonfarm business 

sector—the ratio of real output to labor hours worked—

is a key input into CBO’s forecast of real output growth. 

Although growth in labor productivity fluctuates widely 

from quarter to quarter, its average growth rate tends 

17. For more information about what caused recent recoveries to be 

slow, see Congressional Budget Office, The Slow Recovery of the 

Labor Market (February 2014), www.cbo.gov/publication/45011.

18. Another change that caught most forecasters by surprise was 

the reduction in the volatility of GDP growth beginning in the 

mid-1980s. In particular, from the mid-1980s until the 2007–

2009 recession, quarterly movements in real GDP growth were 

more muted compared with previous decades. See, for example, 

Jordi Galí and Luca Gambetti, “On the Sources of the Great 

Moderation,” American Economic Journal: Macroeconomics, vol. 1, 

no. 1 (January 2009), pp. 26–57, https://tinyurl.com/y65mv7fj.

to remain stable over long periods of time. The stability 

of that average typically helps forecasters estimate real 

output growth over longer time horizons. However, three 

shifts in labor productivity trends have contributed to 

errors in projecting real output growth (see Figure 2).

The first shift occurred in 1974, stemming in part from 

the 1973 recession. Whereas productivity had grown at 

an average rate of 2.8 percent per year over the previous 

25 years, it grew by an average of only about 1.5 percent 

per year through the mid-1990s. Partly because most 

forecasters in the 1970s expected that the productivity 

trend of the previous decades would prevail, their forecasts of real output growth in the latter half of the 1970s 

turned out to be too optimistic (see Figure 3).

The second shift occurred in 1997, when growth in labor 

productivity in the nonfarm business sector accelerated, 

averaging more than 3 percent per year for nearly a 

decade. For the first several years of that period, forecasters underestimated the trend of productivity growth, 

which partly explains why their projections of the 

economy’s growth rate were too low and their projections 

Figure 2.

Trends in Labor Productivity

Average Annual Percentage Change

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1948–

1973

1974–

1996

1997–

2005

2006–

2018

Source: Congressional Budget Office, using data from the Bureau of 

Labor Statistics.

Data show the average annual growth of labor productivity in the 

nonfarm business sector. Labor productivity equals real (inflationadjusted) output divided by the total number of hours worked.
14 CBO’s Economic Forecasting Record: 2019 Update October 2019

of inflation in the output price index were too high.19

19. See Spencer Krane, “An Evaluation of Real GDP Forecasts: 

1996–2001,” Economic Perspectives, vol. 27, no. 1 (Federal 

Reserve Bank of Chicago, January 2003), pp. 2–21, http://

tinyurl.com/y8wadllm; and Scott Schuh, “An Evaluation 

The acceleration in labor productivity stemmed from 

a pickup in technological progress (especially in 

of Recent Macroeconomic Forecast Errors,” New England 

Economic Review (Federal Reserve Bank of Boston, January/

February 2001), pp. 35–56, http://tinyurl.com/ych7zk8d. 

Figure 3.

Errors in Forecasts of Real Output Growth

Percentage Points

Five-Year Forecast Errors

Two-Year Forecast Errors

1976–1977 1981–1982 1986–1987 1991–1992 1996–1997 2001–2002 2006–2007 2011–2012 2016–2017

1976–1977 1981–1982 1986–1987 1991–1992 1996–1997 2001–2002 2006–2007 2011–2012 2016–2017

-4

-2

0

2

4

6

-4

-2

0

2

4

6

CBO Administration Blue Chip Consensus

1976–1980 1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2010 2011–2015

Sources: Congressional Budget Office; Office of Management and Budget; Wolters Kluwer, Blue Chip Economic Indicators; Bureau of Economic 

Analysis.

The measure of real output is gross national product in years before 1992 and gross domestic product in 1992 and later years. Positive errors 

represent overestimates. The dots shown on the horizontal axis indicate that the forecast period overlapped a recession by six months or more. The 

years indicate the time span covered by each of the forecast errors shown in the figure.
October 2019 CBO’s Economic Forecasting Record: 2019 Update 15

information technology) and an increase in the amount 

of capital per worker as firms invested heavily in new 

technology. 

The third shift occurred in 2006, when the average 

growth of labor productivity slowed to 1.3 percent per 

year through 2018, for reasons that are not fully understood. The slowdown partly reflects cyclical factors 

related to the severe recession that occurred from 2007 

to 2009 and the ensuing weak recovery. In addition, 

the growth of the labor force decelerated, which in turn 

slowed the growth of investment and of capital services. 

That slowdown in investment may have also reduced 

the rate at which businesses could introduce new technologies into the production process. Some research 

suggests that other long-term structural problems might 

be impeding the rate at which new technologies diffuse 

through industries.20

In general, shifts in labor productivity are difficult to 

forecast. Such shifts are typically the result of changes in 

capital accumulation, changes in educational attainment, 

and technological innovation—factors that are difficult 

to forecast and that are only easily identified several years 

after the fact. Consequently, if CBO and other forecasters make incorrect inferences about the percentage of 

the labor force receiving higher education degrees, for 

example, that error affects their projections of productivity growth and, by extension, real output growth.

Changes in Crude Oil Prices

Crude oil is an important energy source in the United 

States, with petroleum accounting for more than onethird of total energy consumption.21 As a result, crude 

oil prices are a major component of consumer price 

inflation. Relative to overall prices, crude oil prices are 

volatile, and they fluctuate widely in response to economic and geopolitical developments in oil-producing 

20. See Ryan A. Decker and others, Declining Business Dynamism: 

Implications for Productivity? Hutchins Center Working Paper 23 

(Brookings Institution, September 2016), http://tinyurl.com/

lv9cs9h; and Dan Andrews, Chiara Criscuolo, and Peter N. Gal, 

The Global Productivity Slowdown, Technology Divergence, and 

Public Policy: A Firm Level Perspective, Hutchins Center Working 

Paper 24 (Brookings Institution, September 2016), http://

tinyurl.com/km6942w.

21. See Energy Information Administration, Monthly Energy 

Review (June 2019), Table 1.3, https://go.usa.gov/xyVJg (PDF, 

2.48 MB).

countries (see Figure 4). Some of the largest errors in 

forecasting CPI inflation can be attributed to forecasters’ 

inability to predict major changes in crude oil prices. For 

example, the rise in the price of oil in the late 1970s and 

early 1980s probably contributed to the sizable underprediction of inflation by CBO, the Administration, and 

the Blue Chip consensus; all three forecasters underpredicted the rise in inflation over that time period when 

they completed their forecasts at the end of the 1970s 

(see Figure 5).

Large changes in crude oil prices reflect producers’ and 

consumers’ limited capacity to quickly adjust supply 

and demand in response to changing market conditions.22 Fluctuations in oil prices are often difficult to 

forecast because markets for petroleum products can 

be sensitive to developments that forecasters cannot 

reasonably be expected to predict. For example, during 

the 1973–1981 period, oil prices spiked in response to 

the oil embargo imposed by the Organization of Arab 

Petroleum Exporting Countries (1973 to 1974), the 

Iranian Revolution (1979), and the start of the Iran–Iraq 

War (1980). Those developments affected forecasters’ 

ability to correctly forecast CPI inflation.

Political factors remain a source of uncertainty, but they 

appear to have become less important in explaining 

volatility. Recently, oil prices rose steeply in the lead-up 

to the 2007–2009 recession and fell sharply in 2014 and 

2015 because of shifts in global supply and demand as 

well as technological changes, such as horizontal drilling 

and hydraulic fracturing. 

The Persistent Decline in Interest Rates

Interest rates have trended downward since the early 

1980s (see Figure 6 on page 18). Although that decline 

is partly attributable to a lower average rate of inflation 

over the past two decades, the effect persists even after 

taking into account the change in prices. Recent research 

has identified several factors that may have contributed 

to the decline in real interest rates: the aging of the 

population, increased income inequality, a trend toward 

22. In the near term, consumers are constrained by the existing 

energy efficiency of their homes, places of work, and modes of 

transportation; producers are constrained by their equipment, 

technology, and the availability and accessibility of natural 

resources. For further discussion, see Congressional Budget 

Office, Energy Security in the United States (May 2012), 

www.cbo.gov/publication/43012.
16 CBO’s Economic Forecasting Record: 2019 Update October 2019

Figure 4.

The Effect of Oil Prices on Consumer Price Inflation

CPI-U

CPI-U Without Energy Prices

0

25

50

75

100

125

−4

0

4

8

12

16

1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016

1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016

Percentage Change Consumer Price Index With and Without Energy Prices b

2012 Dollars per Barrel

Real Cost of Crude Oil a

Sources: Congressional Budget Office; Bureau of Labor Statistics; Bureau of Economic Analysis.

Vertical bars indicate the duration of recessions. A recession extends from the peak of a business cycle to its trough.

CPI-U = consumer price index for all urban consumers.

a. The real cost of crude oil is the refiners’ cost of acquiring crude oil divided by the price index for personal consumption expenditures, excluding 

prices for food and energy.

b. The major components of energy prices in the CPI-U are motor fuel (which is primarily composed of petroleum products), electricity, and natural gas 

purchased from utilities.
October 2019 CBO’s Economic Forecasting Record: 2019 Update 17

Figure 5.

Errors in Forecasts of Consumer Price Inflation

Percentage Points

Five-Year Forecast Errors

Two-Year Forecast Errors

1976–1977 1981–1982 1986–1987 1991–1992 1996–1997 2001–2002 2006–2007 2011–2012 2016–2017

1976–1977 1981–1982 1986–1987 1991–1992 1996–1997 2001–2002 2006–2007 2011–2012 2016–2017

-6

-4

-2

0

2

4

6

-6

-4

-2

0

2

4

6

CBO Administration Blue Chip Consensus

1976–1980 1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2010 2011–2015

Sources: Congressional Budget Office; Office of Management and Budget; Wolters Kluwer, Blue Chip Economic Indicators; Bureau of Labor Statistics.

Most forecast errors are errors in forecasting the consumer price index for all urban consumers, but some are errors in forecasting the consumer price 

index for urban wage earners and clerical workers. For details on the underlying data, see the appendix. 

Positive errors represent overestimates. The dots shown on the horizontal axis indicate that the forecast period overlapped a recession by six months 

or more. The years indicate the time span covered by each of the forecast errors shown in the figure. 
18 CBO’s Economic Forecasting Record: 2019 Update October 2019

slower output growth, and increased saving among 

emerging market economies.23

Over the past two decades, forecasters have underestimated the effect of those factors on interest rates, and 

they did not anticipate the extent or persistence of the 

eventual decline. Thus, all forecasters have tended to 

make sizable overpredictions of both short- and longterm interest rates since the early 2000s (see Figure 7).

The Decline in the Labor Share

Labor share refers to the total compensation paid to 

labor as a percentage of GDP. The labor share is composed of several components, the largest of which is total 

wages and salaries paid to employees, which accounts for 

about 80 percent of all labor income. Therefore, misestimates of the labor share typically result in misestimates 

of wages and salaries. 

23. See Lukasz Rachel and Thomas D. Smith, “Are Low Real 

Interest Rates Here to Stay?” International Journal of Central 

Banking (September 2017), pp. 1–42, www.ijcb.org/journal/

ijcb17q3a1.htm; and Council of Economic Advisers, Long-Term 

Interest Rates: A Survey (July 2015), https://go.usa.gov/xyVS3.

Since 2000, the labor share has experienced a structural 

decline for reasons that are only partially understood 

(see Figure 8). One theory is that globalization may 

have increased incentives for businesses to move their 

production of labor-intensive goods abroad.24 Another 

is that technological innovation may have increased 

the returns to capital more than it has increased the 

returns to labor. Either way, forecasters at the start of 

the millennium projected the labor share to stabilize or 

return to its historical average. That expectation resulted 

in upwardly biased projections of wage and salary growth 

(see Figure 9 on page 21).

Transient factors can also cause movements in the labor 

share and lead to forecast misestimates. One such factor 

is the downward shift in the number of employees 

enrolling in employment-based health insurance plans 

that occurred in the late 1980s and early 1990s.25 The 

decline in enrollment was largely the result of rising 

employee premiums and stagnant employer contributions, which reflected the rising cost of medical care 

during that time period. Employees who declined 

employment-based health insurance implicitly reduced 

their total compensation, because employers’ payments 

toward health insurance premiums are counted in total 

employee compensation. That development led forecasters to overestimate labor’s share of income and contributed to overestimates of the growth in wages and salaries 

between 1989 and 1994.

Data Revisions

Many of the data series analyzed in this report are 

periodically revised in response to new data, methods, 

and definitions. For example, the Bureau of Economic 

Analysis (BEA) periodically issues comprehensive 

revisions of its national income and product accounts, 

which contain historical data on real output growth and 

growth of wages and salaries. Those data revisions affect 

24. See, for example, Michael W. L. Elsby, Bart Hobijn, and Aysegul 

Sahin, “The Decline of the U.S. Labor Share,” Brookings Papers 

on Economic Activity, vol. 44, no. 2 (Fall 2013), pp. 1–63, https://

brook.gs/2VCVbyx.

25. For information about changes in employers’ contributions to 

health insurance during the late 1990s, see David M. Cutler, 

Employee Costs and the Decline in Health Insurance Coverage, 

Working Paper 9036 (National Bureau of Economic Research, 

July 2002), www.nber.org/papers/w9036. 

Figure 6.

The Persistent Decline in Interest Rates

Percent

0

5

10

15

1980 1985 1990 1995 2000 2005 2010 2015

10-Year Treasury Note Rate

3-Month Treasury Bill Rate

Source: Congressional Budget Office, using data from the Federal 

Reserve.
October 2019 CBO’s Economic Forecasting Record: 2019 Update 19

Figure 7.

Errors in Forecasts of the 10-Year Treasury Note Rate

Percentage Points

Five-Year Forecast Errors

Two-Year Forecast Errors

1976–1977 1981–1982 1986–1987 1991–1992 1996–1997 2001–2002 2006–2007 2011–2012 2016–2017

1976–1977 1981–1982 1986–1987 1991–1992 1996–1997 2001–2002 2006–2007 2011–2012 2016–2017

-4

-2

0

2

4

-4

-2

0

2

4

CBO Administration Blue Chip Consensus

1976–1980 1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2010 2011–2015

Sources: Congressional Budget Office; Office of Management and Budget; Wolters Kluwer, Blue Chip Economic Indicators; Federal Reserve. 

Positive errors represent overestimates. The dots shown on the horizontal axis indicate that the forecast period overlapped a recession by six months 

or more. The years indicate the time span covered by each of the forecast errors shown in the figure.
20 CBO’s Economic Forecasting Record: 2019 Update October 2019

the computation of forecast errors and, by extension, 

measures of forecast quality. 

One way that data revisions affect the computation 

of forecast errors is by creating a wedge between the 

currently available data and the data that were available 

when the forecast was completed. For example, the root 

mean square error of CBO’s two-year forecasts of real 

output growth is 1.3 percentage points if calculated from 

the most recently available data (as shown in Table 1 on 

page 2)—but is 1.2 percentage points if calculated 

from the real-time data available immediately after the 

conclusion of the two-year horizon. Although some 

recent literature suggests that real-time data may be more 

appropriate for that kind of analysis, CBO uses the most 

recently available data.26 That decision simplifies the 

26. See, for example, Tom Stark and Dean Croushore, “Forecasting 

With a Real-Time Data Set for Macroeconomists,” Journal of 

Macroeconomics, vol. 24, no. 4 (December 2002), pp. 507–531, 

https://doi.org/10.1016/S0164-0704(02)00062-9. 

analysis and helps account for definitional changes that 

affect the interpretation of certain data series over time.27

In addition to affecting the analysis of forecast errors 

after the fact, data revisions can affect a forecaster’s 

projections in real time. For example, BEA made several 

downward revisions to estimates of real GDP growth 

during the 2007–2009 recession (see Figure 10). When 

CBO prepared its baseline forecast in January 2009, 

real GDP had reportedly fallen by an annualized rate of 

0.5 percent during the third quarter of 2008; however, 

revised data now show a 2.1 percent drop during that 

quarter. Similarly, the latest revisions show that the average annual growth of real GDP was nearly one-quarter of 

a percentage point lower during the recession than BEA 

initially reported in January 2010. Had CBO and other 

forecasters known the true state of the economy at the 

time of their forecast, their projections probably would 

have been different.

Quantifying the Uncertainty of CBO’s 

Economic Forecasts

To quantify the uncertainty of its economic projections, 

CBO most frequently relies on the root mean square 

error of its historical forecasts. For each variable, CBO 

computes root mean square errors at the one-, three-, 

and four-year time horizons, in addition to the two- and 

five-year time horizons presented in this report.28 Those 

root mean square errors are applied symmetrically to the 

agency’s baseline projections to produce the range of the 

middle two-thirds of outcomes. 

Such analysis has informed the uncertainty ranges 

surrounding CBO’s projections of real GDP in recent 

27. For example, business and government spending on computer 

software was once treated as spending for an intermediate 

good—that is, an input into the production process—and thus 

did not count as a component of GDP, which measures only 

final spending. But in 1999, BEA reclassified such spending as 

investment, which is a category of final spending. That same 

year, BEA adopted new methods for calculating the price indexes 

for various categories of consumption. Largely as a result of 

those changes, BEA increased its estimates of growth in real 

GDP for the 1980s and 1990s. In particular, BEA’s estimates of 

average annual growth in real GDP from 1992 to 1998 rose by 

0.4 percentage points, and inflation in the GDP price index for 

those years was revised downward by 0.1 percentage point per 

year.

28. CBO’s root mean square errors for forecasts of real output growth 

at the one-, three-, and four-year horizons are 1.1, 1.2, and 

1.3 percentage points, respectively.

Figure 8.

Labor’s Share of GDP

Percentage of GDP

56

57

58

59

60

61

62

63

1965 1975 1985 1995 2005 2015

0

Source: Congressional Budget Office; Bureau of Economic Analysis.

Labor income is the sum of employees’ compensation and CBO’s 

estimate of proprietors’ income that is attributable to labor. 

Vertical bars indicate the duration of recessions. A recession extends 

from the peak of a business cycle to its trough.

GDP = gross domestic product.
October 2019 CBO’s Economic Forecasting Record: 2019 Update 21

Figure 9.

Errors in Forecasts of the Growth in Wages and Salaries

Percentage Points

Five-Year Forecast Errors

Two-Year Forecast Errors

1976–1977 1981–1982 1986–1987 1991–1992 1996–1997 2001–2002 2006–2007 2011–2012 2016–2017

1976–1977 1981–1982 1986–1987 1991–1992 1996–1997 2001–2002 2006–2007 2011–2012 2016–2017

-4

-2

0

2

4

6

8

-4

-2

0

2

4

6

8

CBO Administration

1976–1980 1981–1985 1986–1990 1991–1995 1996–2000 2001–2005 2006–2010 2011–2015

Sources: Congressional Budget Office; Office of Management and Budget; Bureau of Economic Analysis.

Positive errors represent overestimates. The dots shown on the horizontal axis indicate that the forecast period overlapped a recession by six months 

or more. The years indicate the time span covered by each of the forecast errors shown in the figure.
22 CBO’s Economic Forecasting Record: 2019 Update October 2019

editions of The Budget and Economic Outlook. Publishing 

those ranges helps illustrate the breadth of possible 

outcomes and the degree of certainty underlying CBO’s 

economic forecasts. For example, CBO estimated in 

August 2019 that there is approximately a two-thirds 

chance that the average annual growth rate of real GDP 

will be between 0.7 percent and 3.3 percent over the 

next five years (see Figure 11). Its central estimate is 

2.0 percent.

Sometimes CBO uses the two-thirds spread instead of 

the root mean square error to quantify the uncertainty 

of its current economic projections. When the ratio of 

the mean error to the root mean square error is greater 

than or equal to 0.3, CBO estimates that about twothirds of actual values will fall within a range of plus or 

minus one-half of the two-thirds spread of forecasted 

values. For example, CBO’s two-year forecasts of 10-year 

Treasury notes have a mean error of 0.4 percentage 

points and a root mean square error of 0.8 percentage 

points (see Table 1 on page 2). Because the ratio 

of those numbers is greater than 0.3, CBO uses the 

two-thirds spread to estimate that there is a two-thirds 

chance that the average interest rate on 10-year Treasury 

notes will fall within a range of roughly 0.6 percentage 

points above or below the agency’s central estimate of 

2.2 percent.

Forecast errors for economic variables have implications 

for the budget. For example, according to CBO’s rules 

of thumb, if interest rates were 0.1 percentage point 

higher than projected for five consecutive years (and 

all else remained constant), the budget deficit over that 

period would be an estimated $46 billion higher relative 

to the baseline budget projection. Similarly, if inflation 

was 0.1 percentage point higher than projected for five 

consecutive years (and all else remained constant), the 

budget deficit would be an estimated $27 billion higher 

relative to the baseline budget projection.29

29. See Congressional Budget Office, How Changes in Economic 

Conditions Might Affect the Federal Budget (June 2018), 

www.cbo.gov/publication/54052.

Figure 10.

The Effect of Data Revisions on Real Growth of Gross Domestic Product

Percentage Change From Previous Year

June 2019 Actuals

January 2009 Forecast

January 2010 Forecast

−4

−2

0

2

4

6

2006 2008 2010 2012 2014

January 2010 Actuals

January 2009 Actuals

Sources: Congressional Budget Office; Bureau of Economic Analysis.
October 2019 CBO’s Economic Forecasting Record: 2019 Update 23

Figure 11.

The Uncertainty of CBO’s Projections of Real GDP

Trillions of 2012 Dollars

10

12

14

16

18

20

22

1999 2003 2007 2011 2015 2019 2023

0

Real GDP

Two-Thirds of 

Possible Outcomes

CBO estimates that—if 

the errors in the agency’s 

current economic forecast 

are similar to those in its 

previous forecasts—there is 

approximately a two-thirds 

chance that the average 

annual rate of real GDP 

growth will be between 

0.7 percent and 3.3 percent 

over the next five years.

Sources: Congressional Budget Office; Bureau of Economic Analysis.

Real values are nominal values that have been adjusted to remove the effects of changes in prices. The shaded area around CBO’s baseline projection 

of real GDP illustrates the uncertainty of that projection. The area is based on the errors in CBO’s one-, two-, three-, four-, and five-year projections of 

the average annual growth rate of real GDP for calendar years 1976 through 2018.

Values for real GDP from 1999 to 2018 (the thin line) reflect revisions to the national income and product accounts that the Bureau of Economic 

Analysis released on July 26, 2019. Values from 2018 to 2029 (the thick line) reflect the data available when the projections were made earlier in July.

GDP = gross domestic product.

Appendix: The Data CBO Uses to Evaluate 

Its Economic Forecasting Record

This appendix provides an overview of the 

forecast and historical data the Congressional 

Budget Office uses to evaluate its forecasting record. In the report, CBO analyzes its 

errors in forecasting output growth, the unemployment 

rate, inflation, interest rates, and changes in wages 

and salaries. Forecast values are primarily compiled 

from CBO’s annual Budget and Economic Outlook, the 

Administration’s annual budget documents, and Blue 

Chip Economic Indicators reports from the first quarter 

of each calendar year. Actual values for each variable are 

based on the latest available data from various agencies, 

including the Bureau of Economic Analysis (BEA) and 

the Bureau of Labor Statistics (BLS). 

Forecasts Included in This Evaluation

This report evaluates forecasts published between 

1976—the first year CBO made economic projections—

and 2017. The agency’s forecasts are typically published 

in the first quarter of each calendar year. From 1976 to 

1984, however, those reports did not regularly include 

CBO’s forecasts of wages and salaries, so this analysis incorporates some unpublished forecasts of those 

amounts.

For comparison, this report also evaluates the 

Administration’s forecasts made between 1976 and 

2017, all but two of which were taken from the Office 

of Management and Budget’s annual budget documents. 

In Presidential transition years, forecasts are taken 

from the budget documents of incoming Presidential 

administrations, with two exceptions: First, the Reagan 

Administration’s 1981 economic projections were based 

on revisions of the Carter Administration’s final budget and were released separately. Second, the Clinton 

Administration chose not to make its own economic 

projections before releasing its 1993 budget, adopting 

instead CBO’s economic projections as the basis of its 

budget. As a result, errors in the 1993 forecast are the 

same for both forecasters.

The Blue Chip Economic Indicators was published beginning in 1976, but because of data constraints this report 

only analyzes Blue Chip forecasts dating back to 1979. 

Although the Blue Chip consensus is published monthly, 

only the March and October forecasts extend beyond 

two years. As a result, this analysis uses the January publication to obtain two-year forecasts of each economic 

variable and the March publication to obtain five-year 

forecasts. One exception is the five-year forecasts published in 1980, which were released in May. Because 

the Blue Chip consensus forecasts are only available 

starting in 1979, comparisons of forecast quality shown 

in Table 1 on page 2 do not include CBO’s forecasts 

produced from 1976 through 1978.

The Federal Reserve’s forecasts are taken from two 

sources. The first is the Federal Reserve’s Greenbook, 

a publication prepared by the staff of its Board of 

Governors. The Greenbook contains two-year projections for a wide range of economic variables, but it is not 

released to the public until several years after the forecast is made. As a result, this analysis only incorporates 

Greenbook forecasts from 1979 to 2013. The second 

source is the economic projections prepared by individual members of the Federal Reserve’s Federal Open 

Market Committee (FOMC). The ranges and central 

tendencies of those forecasts encompass a smaller set of 

variables, but they have been published regularly since 

2007. This analysis uses the midpoint of the central tendency of the FOMC forecasts in 2014 and 2015 and the 

median of the FOMC forecasts in 2016 and 2017. All 

of the Federal Reserve forecasts used in this analysis were 

issued in January or February of the initial forecast year 

or in December of the preceding year.

Output Growth

Growth of real (inflation-adjusted) and nominal output 

is computed from calendar year averages of the most 

recently available quarterly data published by BEA. To 

calculate the average annual growth rate over two- and 
26 APPENDIX: THE DATA CBO USES TO EVALUATE ITS ECONOMIC FORECASTING RECORD October 2019

five-year time horizons, CBO computes the geometric 

average of each series over the relevant sample period.1

One major methodological shift occurred in 1991, when 

BEA changed its featured measure of output from gross 

national product (GNP) to gross domestic product 

(GDP). In response, most economic forecasters switched 

to forecasting GDP starting in 1992. As a result, for this 

analysis CBO computes forecast errors for GNP growth 

before 1992 and forecast errors for GDP growth between 

1992 and 2017.

Unemployment Rate

The unemployment rate is computed from calendar year 

averages of monthly data published by BLS. To calculate 

the average unemployment rate over two- and five-year 

time horizons, CBO computes the arithmetic mean of 

the annual unemployment rate series over the relevant 

sample period. A major methodological shift occurred in 

1994, when BLS updated and modernized its Current 

Population Survey, which is used to measure the unemployment rate. Because the methodology was changed in 

February of that year, some forecasters included projections of unemployment on the new basis as well as the 

old basis. For this report, CBO computes forecast errors 

relative to the old basis for forecasts between 1976 and 

1994 and relative to the new basis for forecasts made 

from 1995 to 2017.

Inflation

CBO computes inflation in the consumer price index 

(CPI) from calendar year averages of monthly data 

published by BLS. To calculate the average annual 

growth rate over two- and five-year time horizons, CBO 

computes the geometric average of each series over the 

relevant sample period. In January 1978, BLS began 

publishing the CPI-U (the price index for all urban 

consumers) in addition to the CPI-W (the price index 

for urban wage earners and clerical workers). Although 

annual fluctuations in the two price indexes are typically 

very similar, they occasionally diverge. To account for 

those divergences, this report compares CPI-U forecasts 

with CPI-U actuals and CPI-W forecasts with CPI-W 

actuals.

1. A geometric average is calculated by first multiplying the growth 

rates for each year and then taking the nth root of that product. 

The geometric average is the appropriate measure for averaging 

growth rates.

The introduction of the CPI-U also resulted in different approaches to forecasting consumer price inflation. 

The Administration published forecasts of the CPI-W 

from 1976 to 1991 because that measure was used to 

index most of the federal government’s spending for 

entitlement programs, but it switched to forecasting the 

CPI-U starting in 1992. By contrast, CBO based all but 

four of its inflation forecasts after 1978 on the CPI-U. 

The exceptions are CBO’s forecasts from 1986 to 1989, 

which were based on the CPI-W. The Blue Chip consensus and the Federal Reserve have always forecasted the 

CPI-U.

In this report, CBO also analyzes errors in the inflation 

differential—the difference between CPI inflation and 

inflation in the output price index. To compute growth 

in the output price index, CBO uses calendar year 

averages of the most recently available quarterly data 

published by BEA. Similar to its computations of real 

output growth, the agency’s computations of the inflation differential use the GNP price index for forecasts 

made before 1992 and the GDP price index for forecasts 

made between 1992 and 2017. CBO subtracts growth in 

the output price index from growth in CPI inflation to 

compute the inflation differential.

Interest Rates

CBO computes the interest rate on 3-month Treasury 

bills from calendar year averages of monthly data published by the Federal Reserve. To calculate the average 

interest rate over two- and five-year time horizons, CBO 

computes the geometric mean of the annual interest 

rate series over the relevant sample period. The agency’s 

analysis of the interest rate on 3-month Treasury bills 

focuses on two main measures: the new-issue rate and 

the secondary-market rate. 

The new-issue rate reflects the interest that would be 

earned by an investor who purchased a bill at auction 

and held it to maturity. The secondary-market rate 

corresponds to the price of 3-month bills traded outside 

of Treasury auctions. Such transactions occur continually 

in markets that have many more traders than there are 

bidders in Treasury auctions. The difference between the 

calendar year averages of the two series is small, which 

makes them easily comparable across time horizons.

The Administration published forecasts of the new-issue 

rate until 2001, when it switched to forecasting the 

secondary-market rate. Unlike the Administration, 
APPENDIX: THE DATA CBO USES TO EVALUATE ITS ECONOMIC FORECASTING RECORD CBO’S ECONOMIC FORECASTING RECORD: 2019 UPDATE 27

CBO has always forecasted the secondary-market rate. 

The Blue Chip consensus, by contrast, has alternated 

between the two rates and, in 1981, even projected a 

third—the six-month commercial paper rate. The Blue 

Chip consensus forecast the new-issue rate from 1982 

to 1985, the secondary-market rate from 1986 to 1991, 

and the new-issue rate again from 1992 to 1997. Since 

March 1997, the Blue Chip consensus has forecast the 

secondary-market rate.

As part of this analysis, CBO also computes errors in 

forecasting the real interest rate on 3-month Treasury 

bills. To do that, the agency deflates each forecaster’s 

annualized projections of nominal interest rates by that 

forecaster’s annualized projections of CPI inflation. 

Although the forecasters used different measures of inflation and short-term interest rates, the resulting real interest rate forecasts are comparable across time horizons.

CBO’s analysis of long-term interest rates focuses on two 

measures: the 10-year Treasury note rate, published by 

the Federal Reserve, and Moody’s Aaa corporate bond 

rate. For each series, CBO uses calendar year averages of 

the latest available monthly data. To calculate the average 

interest rate over two- and five-year time horizons, CBO 

computes the geometric average of the annual interest 

rate series over the relevant sample period.

Both the 10-year Treasury note and the Aaa corporate 

bond are widely considered low-risk financial securities. 

Although the return on 10-year Treasury notes is guaranteed by the federal government, the return on Aaa 

corporate bonds depends on the solvency of the corporations deemed most likely to pay back their financial obligations. Because investors perceive the government to be 

slightly more creditworthy than the safest corporations, 

the 10-year Treasury note rate is consistently slightly 

lower than the Aaa corporate bond rate. Annual fluctuations in the two series are highly correlated, however, 

making them roughly comparable across time horizons.

CBO projected the Aaa corporate bond rate in 1984 and 

1985 but since then has projected the 10-year Treasury 

note rate. The Administration has always published 

forecasts of the 10-year Treasury note rate. The Blue Chip 

consensus forecast the Aaa corporate bond rate until 

1996, when it switched to the 10-year Treasury note rate. 

Because of data constraints, it is not possible to compare 

forecasts made before 1984.

Wages and Salaries

The growth of wages and salaries is computed from 

calendar year averages of the most recently available 

quarterly data published by BEA. To calculate the 

average annual growth rate over two- and five-year time 

horizons, CBO computes the geometric average of each 

series over the relevant sample period. Because the Blue 

Chip Economic Indicators report does not publish a consensus forecast of the growth in wages and salaries, it is 

omitted from the forecast analysis of that variable.

In this report, CBO also analyzes errors in projections of 

wages and salaries as a share of output. For the wages and 

salaries component, the agency uses calendar year averages of the most recently available quarterly data published by BEA. For the output component, the agency 

uses nominal GNP for forecasts made between 1976 

and 1991 and nominal GDP for forecasts made between 

1992 and 2017. The agency then divides the amount of 

wages and salaries by the amount of nominal output to 

compute wages and salaries as a share of output. CBO 

uses the arithmetic average to compute the average 

share of wages and salaries over two- and five-year time 

horizons.
List of Tables and Figures

Table

1. Summary Measures for Two-Year and Five-Year Forecasts, by Sample Years 2

Figures

1. Root Mean Square Errors of Two-Year Forecasts Made Near Business Cycle Peaks 12

2. Trends in Labor Productivity 13

3. Errors in Forecasts of Real Output Growth 14

4. The Effect of Oil Prices on Consumer Price Inflation 16

5. Errors in Forecasts of Consumer Price Inflation 17

6. The Persistent Decline in Interest Rates 18

7. Errors in Forecasts of the 10-Year Treasury Note Rate 19

8. Labor’s Share of GDP 20

9. Errors in Forecasts of the Growth in Wages and Salaries 21

10. The Effect of Data Revisions on Real Growth of Gross Domestic Product 22

11. The Uncertainty of CBO’s Projections of Real GDP 23
About This Document

The Congressional Budget Office regularly evaluates the accuracy of its economic forecasts 

by comparing them with the economy’s actual performance and with others’ forecasts. Such 

evaluations help guide CBO’s efforts to improve the quality of its forecasts and, as background 

information, are also intended to assist Members of Congress in using the agency’s estimates. 

In keeping with CBO’s mandate to provide objective, impartial analysis, the report makes no 

recommendations.

Edward Gamber and Adam Staveski (formerly of CBO) prepared the report with guidance from 

Robert Arnold, John Kitchen, and Jeffrey Werling. Kim Kowalewski (formerly of CBO) and John 

McClelland provided helpful comments, as did Tara Sinclair of George Washington University. 

(The assistance of an external reviewer implies no responsibility for the final product, which rests 

solely with CBO.) Erin Deal and Sarah Robinson fact-checked the report. 

Jeffrey Kling and Robert Sunshine reviewed this report. The editor was Rebecca Lanning, and 

the graphics editor was Casey Labrack. An electronic version is available on CBO’s website 

(www.cbo.gov/publication/55505).

CBO continually seeks feedback to make its work as useful as possible. Please send any comments 

to communications@cbo.gov.

Phillip L. Swagel

Director

October 2019