CBO’s Economic Forecasting Record: 2025 Update

Summary

The Congressional Budget Office regularly publishes reports presenting its baseline projections of what the federal budget and the economy would look like in the current year and over the next 10 years if laws governing taxes and spending generally remained unchanged. CBO regularly analyzes the errors in its past economic forecasts to evaluate the quality of those forecasts and isolate the effects of such errors on the agency’s budget projections.

In this report, CBO evaluates its economic forecasts from as early as 1976. The economic projections examined span the first two years and five years of each baseline projection period. CBO compares the forecasts with analogous ones from the Administration, the Survey of Professional Forecasters (SPF), and the Blue Chip consensus.1 The comparisons help identify areas in which CBO has tended to make larger errors than other forecasters. They also indicate the extent to which imperfect information may have caused forecasters to misperceive patterns or turning points in the economy.

How CBO Evaluates the Quality of Its Economic Forecasts

To evaluate the quality of its economic forecasts, CBO measured three of their characteristics: accuracy, centeredness, and dispersion.

• Accuracy refers to the amount by which actual values differ from projected ones. To measure accuracy, CBO used the average absolute error and the root mean square error (RMSE). The average absolute error is the average of the errors without regard to whether they are overestimates or underestimates (the negative signs are removed from underestimates before averaging), so errors in different directions do not offset each other. The RMSE also measures the size of errors after removing the negative signs but, by squaring the errors, places a greater weight on larger deviations.
• Centeredness refers to the tendency of projections to be overestimates or underestimates. To measure centeredness, the agency calculated the average error—that is, the average of the annual forecast errors. Centeredness is inversely related to statistical bias, which quantifies the degree to which a forecaster’s projections are too high or too low over time.
• Dispersion refers to the size in the spread of errors. To measure dispersion, CBO calculated the two-thirds spread of errors, measured as the difference between the endpoints of the range of the errors after removing the one-sixth of errors furthest above the median, or midpoint of the errors, and the one-sixth furthest below the median.

The Quality of CBO’s Economic Forecasts

Forecasts that have an average error of zero are, on average, neither too high nor too low.2 CBO’s forecasts of economic variables considered in this report tend to exhibit small positive average errors—that is, on average, they are too high by small amounts. For many variables, average errors in the agency’s two- and five-year forecasts are small and not statistically significant.3

In general, comparing quality measures across variables is difficult because the magnitudes of variables can differ substantially, and some variables are easier or harder to forecast than others. However, comparing those measures across forecast horizons for a given variable is possible. For example, as measured by the RMSE, CBO’s five-year forecasts of interest rates are less accurate than its two-year forecasts of those variables. For other variables, CBO’s forecasts are as accurate or more accurate at the five-year horizon, as measured by the RMSE.

Comparisons With Other Forecasts

One way to assess the quality of CBO’s forecasts is to compare their accuracy with the accuracy of other forecasts. CBO thus calculated the RMSE for three forecast variables that are particularly important for budget projections: the growth of real output, inflation as measured by the consumer price index (CPI), and the interest rate on 10-year Treasury notes (see Table S-1). (Real values are those that have been adjusted to remove the effects of inflation.) In its two-year forecasts of those variables, CBO was 11 percent more accurate than the Administration, 4 percent more accurate than the Blue Chip consensus, and 7 percent more accurate than the SPF, on average. In its five-year forecasts, CBO was 9 percent more accurate than the Administration and 6 percent more accurate than the Blue Chip consensus. (Five-year forecasts are not available from the SPF.)

Sources of Forecast Errors

Since the 1970s, when the earliest projections included in this analysis were published, forecasters have made large errors in their economic forecasts because of several economic developments:

• Turning points in the cycle of economic activity,
• Changes in labor productivity trends,
• Changes in crude oil prices,
• The downward trend in interest rates,
• The decline in labor income measured as a share of gross domestic product (GDP),
• Data revisions, and
• Effects of the coronavirus pandemic.

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 CPI.

Other developments, such as turning points in the cycle of economic activity, affect the entirety of an economic forecast, and their effects are observable in the error patterns of several variables. For most of the economic variables considered in this analysis, the accuracy of projections from all four sources is lower in periods that overlap with recessions. The unusual circumstances associated with the pandemic—including large swings in real output and employment as well as increased inflation—also resulted in large forecast errors for several of the economic variables examined in this report.

CBO’s Methods for Evaluating Forecasts

To evaluate the quality of its forecasts, CBO examined the errors in its past forecasts and compared them with errors in forecasts made by the Administration, the SPF, and the Blue Chip consensus. For this analysis, CBO reviewed the economic projections that it has published each winter (usually in January) since 1976, which have served as the basis for its baseline budget projections. Each set of projections spanned the current year (that is, the calendar year already underway) and either 5 or 10 subsequent years. CBO selected 10 forecast variables on the basis of their importance to the economic outlook and their relevance to CBO’s budget projections. The agency then calculated each set of forecasts’ accuracy, centeredness, and dispersion over time and compared them with those same characteristics of other forecasts.

Selecting Time Periods for Comparison

This report evaluates CBO’s economic forecasts over the first two years and five years of its baseline projection period. The two-year horizon is most relevant when the agency is preparing its baseline budget projections for the upcoming fiscal year (often called the budget year). CBO evaluates forecasts over the five-year horizon to assess the quality of its longer-term projections.4

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 data about actual economic outcomes. 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 four sets of forecasts. So, although CBO has two-year forecasts of real output growth dating back to 1976, its forecast errors for comparison purposes are computed starting in 1982—the first year comparable SPF data were available.5 Likewise, the final year of forecast analysis depends on the availability of data about actual outcomes. This report incorporates data through the end of 2024, which allows CBO to analyze two-year forecasts that were made through the beginning of 2023 and five-year forecasts that were made through the beginning of 2020. (See the appendix for details.)

Selecting Forecast Variables

CBO selected 10 forecast variables on the basis of their importance to the economic outlook and to projections of revenues, outlays, and deficits.6 Those variables include the following:

• 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 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 by the federal government. (Transfer payments are payments made to a person or organization for which no current or future goods or services are required in return. Federal transfer payments include Social Security and unemployment benefits.)

CBO’s forecast of the unemployment rate affects its projections of inflation, interest rates, and other variables related to the labor market. The agency’s forecast of the unemployment rate also informs its projections of certain outlays, including those for unemployment compensation.

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.7 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, including the tax brackets, are indexed to consumer price indexes).8 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 was higher than anticipated and the output price index grew more slowly than anticipated, the projected deficit would generally be larger than expected.

The projected interest rates on 3-month Treasury bills and 10-year Treasury notes summarize CBO’s forecasts of short- and long-term interest rates, respectively. Interest rates mainly affect the budget through their effect on net outlays for interest—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. According to CBO’s rules of thumb, if interest rates were 0.1 percentage point lower than projected for the next five consecutive years (and all else remained constant), the budget deficit over that period would be an estimated $123 billion lower than the baseline budget projection. As another example, if inflation and interest rates were both 0.1 percentage point higher than projected for the next five consecutive years (and all else remained constant), the budget deficit would be an estimated $105 billion higher than the baseline budget projection.9 In this report, CBO also analyzes errors in the projected real interest rate on 3-month Treasury bills, which is computed by removing the effects of CPI inflation from forecasts of the nominal interest rate on 3-month Treasury bills. Considering such errors isolates interest rate errors from errors in inflation projections.

Finally, CBO examines growth in wages and salaries and the change in those values as a share of output. Wages and salaries are the largest component of national income, and their growth informs CBO’s revenue projections. Analyzing wages and salaries as a share of output offers an approximation of forecasters’ views about the labor share of output and helps isolate errors in projecting wages and salaries from errors in projecting nominal output.

Calculating Forecast Errors

CBO calculates 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 negative error indicates that the forecast value was below the actual value.

The method that CBO uses to calculate forecast errors for this report differs from the method it uses to evaluate its budget projections.10 In those other cases, errors are calculated for a single fiscal year. For example, the error in CBO’s two-year revenue projection for 2018 is the difference (expressed as a percentage) between the actual amount of revenues received in fiscal year 2018 and the revenues projected for that year in January 2017.11 But in this report, errors are calculated over a span of either two or five calendar years. For example, the errors in the two-year forecasts of economic variables made in January 2017 are the errors in the average for 2017 and 2018.

Measuring Forecast Quality

To evaluate the quality of its economic projections, CBO focused on three characteristics: accuracy, centeredness, and dispersion. To measure those characteristics, CBO calculated the average absolute error, the RMSE, the average error, and the two-thirds spread of errors.12 Other measures of forecast quality, such as whether forecasters optimally incorporate all relevant information when making their projections, are harder to assess.13

Accuracy. CBO uses two measures to assess the accuracy of its forecasts: the average absolute error and the RMSE. The average absolute error, which is calculated by averaging the absolute value of the forecast errors, is useful for determining the magnitude of the error regardless of its direction (that is, whether it is positive or negative).

CBO’s main measure of forecast accuracy is the RMSE, which is calculated by taking the root of the mean square error. The mean square error is equal to the sum of a measure of centeredness (the square of the average error) plus a measure of dispersion (the square of the standard deviation of the errors). (The standard deviation is a common measure used to describe the variability in a particular estimate.) That measure places greater weight on instances in which the forecast values deviate significantly from actual values. Unlike the computation of the average error, forecast underestimates and overestimates do not offset one another when the RMSE is computed.

Centeredness. CBO uses the average of forecast errors to measure the centeredness of each variable. The agency uses centeredness to determine whether its forecasts are consistently higher or lower than actual economic outcomes. CBO’s goal is to provide forecasts of economic indicators that lie in the middle of the distribution of possible outcomes.

The average error does not, however, completely describe 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 average error might indicate that all forecasts had small errors, but it can also result from large overestimates and large underestimates that mostly offset one another. CBO uses the average error as its primary measure of statistical bias because it is widely used and easily interpretable.

Dispersion. 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.

Limitations of the Forecast Evaluations

Interpreting forecast errors is not straightforward, for two reasons. First, methods of producing forecasts continue 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 computational and forecasting methods. Although those adjustments improve the quality of forecasts, they can make evaluating the differences between errors in new forecasts and older ones difficult.

The second challenge, which arises in comparing projections of different forecasters, 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.14 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 and the SPF all make their own assumptions about fiscal policy, but the surveys do not report them.

Forecast errors may be affected by those different assumptions about fiscal policy, especially when forecasts are made while policymakers are considering major legislative changes. In early 2009, for example, contributors to the Blue Chip consensus and the SPF reported that they expected additional fiscal stimulus, which implied stronger output growth than would be expected under current law.15 By contrast, CBO’s projections of output growth 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 and SPF forecasts 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 and the SPF economic forecasts to exhibit smaller errors than CBO’s forecast in early 2017. Finally, at the beginning of 2021, many Blue Chip and SPF forecasters probably assumed that further legislative action would occur to address the pandemic and its economic effects, which CBO’s current-law forecast did not take into account. The American Rescue Plan Act of 2021 (P.L. 117-2) became law in March, soon after CBO’s forecast was published.

The Quality of CBO’s Forecasts Across Time Horizons

Although CBO’s forecasts of the examined economic variables exhibit average errors that tend to be positive and small in most cases, there are notable differences between two-year and five-year forecasts (see Figure 1). On average, CBO’s two-year forecasts tend to be more centered than its five-year forecasts. However, CBO’s two-year and five-year forecasts show similar levels of accuracy: The two-year forecasts are more accurate than the five-year forecasts for about half of the economic variables discussed in this report.

Figure 1.

Comparison of CBO’s Two-Year and Five-Year Forecast Quality

Percentage points

Notes

Data source: Congressional Budget Office, using data from the Bureau of Economic Analysis, the Bureau of Labor Statistics, and the Federal Reserve Board of Governors. See www.cbo.gov/publication/61334#data.

a. The measure of output is gross national product in years before 1992 and gross domestic product in 1992 and following years. Real output is nominal output adjusted to remove the effects of inflation.

b. The inflation differential is the difference between growth in the consumer price index and growth in the output price index.

c. The real interest rate is the nominal interest rate deflated by projected growth in inflation as measured by the consumer price index.

Although agency’s two-year and five-year forecasts show similar degrees of accuracy, on average, there are notable differences among some variables. For example, the agency’s five-year forecasts of inflation in the CPI are more accurate than its two-year forecasts of that variable because anticipating short-term fluctuations is often more difficult than identifying long-term trends. However, CBO’s two-year forecasts of interest rates are more accurate than its five-year forecasts of such rates.

The centeredness of the agency’s forecasts also differs among variables. CBO’s two-year forecasts of nominal output growth and growth in wages and salaries are more centered than its five-year forecasts, on average. That pattern does not hold for all variables, however. For example, CBO’s five-year forecasts of interest rates (both for 3-month Treasury bills and for 10-year Treasury notes) are more centered than its two-year forecasts of interest rates.

CBO’s forecasts also tend to exhibit similar amounts of dispersion for most variables across the two- and five-year horizons, but exceptions do occur. For example, CBO’s five-year forecast of the unemployment rate has a larger two-thirds spread of errors than its comparable two-year forecast. Conversely, the agency’s forecasts of CPI inflation and growth of real output have smaller two-thirds spreads over the five-year horizon than over the two-year horizon.

A Comparison of Forecast Quality

CBO compared its two-year economic forecasts with analogous forecasts produced by the Administration, the Blue Chip consensus, and the SPF. (For a comparison of CBO’s two-year forecasts with forecasts made by the Federal Reserve, see Box 2.) CBO compared its five-year economic forecasts with forecasts produced by the Administration and the Blue Chip consensus. Comparisons with the Blue Chip consensus and the SPF are particularly useful because those forecasts incorporate 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.16 In each comparison, the agency examined projections of output growth, the unemployment rate, inflation, interest rates, and growth in wages and salaries. Each set of forecasts displays similar patterns of error over time, but small differences in the magnitude of those errors led to some appreciable differences in measures of forecast quality.

Across two- and five-year horizons, the accuracy of CBO’s economic forecasts is similar to that of forecasts by the Blue Chip consensus and the Administration, although CBO’s projections tend to be more accurate by small amounts. Roughly half of CBO’s two-year forecasts are more accurate than those produced by the SPF (see Table 1 and see Table 2).17

The centeredness of CBO’s two- and five-year forecasts, as measured by average errors, is broadly similar to the centeredness of forecasts made by the Administration and the Blue Chip consensus. The same is true when comparing CBO’s two-year forecasts to those produced by the SPF. However, CBO and the Blue Chip consensus have smaller average errors when forecasting output growth, whereas the Administration produces the most-centered forecasts of interest rates, and the SPF produces the most-centered two-year forecasts of the inflation differential.

There is no consistent pattern in the dispersion of errors in CBO’s forecasts: The spreads of errors are smaller than those in some outside forecasts and larger than those in others. The agency’s forecasts tend to exhibit smaller two-thirds spreads of errors than the Administration’s forecasts do for both two- and five-year forecasts and larger two-thirds spreads than the SPF’s forecasts at the two-year horizon. The agency’s forecasts tend to have smaller two-thirds spreads than those of the Blue Chip consensus at the two-year horizon but larger two-thirds spreads at the five-year horizon. There are exceptions, however, for some variables and forecast horizons. For example, CBO’s two-year forecasts of interest rates on 3-month Treasury bills exhibit a larger two-thirds spread of errors than analogous forecasts from the Blue Chip consensus.

Real and Nominal Output Growth

CBO’s forecasts of real and nominal output growth tend to be more accurate than the Administration’s, except for projections of five-year nominal output growth, which are about the same in terms of accuracy. As measured by the RMSE, CBO’s forecasts of output growth tend to be more accurate than those of the Blue Chip consensus at the two-year horizon but less accurate at the five-year horizon. At the two-year horizon, CBO’s forecasts of real output growth tend to be more accurate by small amounts than those produced by the SPF.

The agency’s forecasts of real and nominal output growth tend to be more centered than the SPF’s forecasts at the two-year horizon and more centered than the Administration’s forecasts across time horizons. CBO’s forecasts of output growth are roughly as centered as those produced by the Blue Chip consensus.

CBO’s forecasts of real and nominal output growth tend to exhibit larger two-thirds spreads than those produced by the SPF at the two-year horizon. The agency’s forecasts of output growth show smaller two-thirds spreads than those produced by the Blue Chip consensus at the two-year horizon and larger spreads at the five-year horizon.

Unemployment

CBO, the Administration, the SPF, and the Blue Chip consensus tend to produce similar forecasts of the unemployment rate with similar accuracy. However, CBO’s two-year forecasts have a slightly larger RMSE than those of other forecasters. The Administration and the SPF have slightly underestimated the unemployment rate over the two-year horizon, and all three sets of forecasts with five-year projections underestimated it over that time horizon. The forecasts show little variation in the two-thirds spread of errors over the two- and five-year horizons, although the Administration’s five-year forecasts show a larger two-thirds spread than those of other forecasters.

Inflation

CBO’s forecasts of CPI inflation and the inflation differential tend to be more accurate than those of other forecasters across time horizons. Over the two-year horizon, the agency’s forecasts of CPI inflation have similar degrees of centeredness compared with those of the Administration, the SPF, and the Blue Chip consensus. Over the five-year horizon, CBO’s forecasts of CPI inflation tend to be more centered than those of the Blue Chip consensus but less centered than those of the Administration. Over the two- and five-year horizons, each set of forecasts has underestimated the inflation differential, except for the two-year forecasts produced by the SPF.

Forecasts of CPI inflation show similar two-thirds spreads across forecasters and time horizons; the Administration’s forecasts exhibit the largest spreads of errors. Forecasts of the inflation differential also generally exhibit similar two-thirds spreads among forecasters; the SPF’s forecasts show the largest spreads over the two-year horizon. All four sets of forecasts underestimated the rise in inflation in 2021 and 2022, which led to large forecast errors for those years.

Interest Rates

Forecasts from all four sources have tended to overpredict actual values for real and nominal interest rates. Forecasts of interest rates show similar degrees of accuracy across forecasters and time horizons. The Administration’s forecasts exhibit the largest RMSEs at the two-year horizon by small amounts; its forecasts of interest rates, however, tend to be the most centered among forecasters and across time horizons. The two-thirds spreads of errors do not follow a clear pattern among forecasts.

Wages and Salaries

CBO’s forecasts of the growth in wages and salaries and the change in wages and salaries as a share of output tend to be more accurate than those produced by the Administration at the two-year horizon. At the five-year horizon, the forecasts exhibit about the same degree of accuracy. (The Blue Chip consensus and the SPF do not provide forecasts of the growth in wages and salaries.) The centeredness and dispersion of CBO’s and the Administration’s forecasts are also similar across time horizons. However, CBO’s two-year forecasts of the growth in wages and salaries show larger two-thirds spreads of errors than those produced by the Administration. Over both time horizons, CBO’s and the Administration’s forecasts of the growth in wages and salaries have been too high, thus exhibiting significant upward bias.

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 cycle of economic activity, changes in labor productivity trends, changes in crude oil prices, the downward trend in interest rates, the declining labor share of output, data revisions, and the effects of the coronavirus pandemic.18 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 cycle of economic activity, have wide-ranging effects on economic forecasts and affect the projections of many variables.

Turning Points in the Cycle of Economic Activity

Peaks and troughs in the normal cycle of economic activity mark the beginning and end of recessions, or periods of significant economic contraction. This analysis covers five recessions and recoveries—those that occurred in 1980, 1981 to 1982, 1990 to 1991, 2001, and 2007 to 2009—as well as the recent 2020 recession, which came in the wake of the pandemic. Although the depth and duration of the recessions differed, all contributed to forecast misestimates that were substantially larger than those made in nonrecession years. For most of the economic variables considered in this analysis, the accuracy of projections from all four sources is lower in periods that overlap with recessions (see Figure 2). Moreover, the largest errors have tended to occur during such periods (see Box 3).

Figure 2.

Root Mean Square Errors of Two-Year Forecasts Made Near Peaks in the Cycle of Economic Activity

Percentage points

Notes

Data source: Congressional Budget Office, using data from the Bureau of Economic Analysis; the Bureau of Labor Statistics; the Federal Reserve Bank of Philadelphia, Survey of Professional Forecasters; the Federal Reserve Board of Governors; the Office of Management and Budget; and Wolters Kluwer, Blue Chip Economic Indicators. See www.cbo.gov/publication/61334#data.

The root mean square errors for recessions are based on forecasts made near peaks in the cycle of economic activity—those published in 1981, 1990, 2001, 2008, and 2020. (Recessions begin just after a peak in economic activity and run through the subsequent trough.) The root mean square errors for other years are based on all two-year forecasts made through 2023, except for the five made near peaks in the cycle of economic activity.

a. The measure of output is gross national product in years before 1992 and gross domestic product in 1992 and following years. Real output is nominal output adjusted to remove the effects of inflation.

b. The real interest rate is the nominal interest rate deflated by projected growth in inflation as measured by the consumer price index.

Forecasters struggle to produce accurate forecasts around the time of economic downturns for three main reasons. Because such downturns cannot be accurately predicted from available information, the first challenge is anticipating when one will occur. During periods of growth, identifying which economic imbalances will ultimately result in a recession is often difficult. Recessions are often precipitated by unforeseeable shocks from factors outside the economy, such as the Iraqi invasion of Kuwait (1990) or the pandemic (2020). Moreover, knowing whether the economy is in a recession is often difficult until well after one has begun. Thus, forecasts made just before a recession tend to be overly optimistic about economic outcomes.

The second challenge is predicting the length and severity of a recession. Recessions often coincide with periods of great uncertainty, both in economic outcomes and in 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 range of possible outcomes.

The final challenge is predicting the speed with which the economy will recover from a recession. 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, except for the recovery from the 2020 recession.19 Failing to predict slower economic recoveries has caused forecasters to overestimate economic growth in the aftermath of economic downturns.20

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 in CBO’s forecast of real output growth. Although growth in labor productivity fluctuates widely from quarter to quarter, the average growth rate tends to remain relatively 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 such growth (see Figure 3).

Figure 3.

Trends in Average Annual Growth in Labor Productivity

Percent

Notes

Data source: Congressional Budget Office, using data from the Bureau of Labor Statistics. See www.cbo.gov/publication/61334#data.

Data show the average annual growth of labor productivity in the nonfarm business sector. Labor productivity equals real output (that is, output adjusted to remove the effects of inflation) divided by the total number of hours worked.

The first shift occurred in 1974, stemming in part from the 1973 recession. Whereas productivity had grown at an average rate of 2.6 percent per year over the previous 25 years, it grew by an average of 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 were too optimistic (see Figure 4).

Figure 4.

Errors in Forecasts of Real Output Growth

Percentage points

Notes

Data source: Congressional Budget Office, using data from the Bureau of Economic Analysis; the Federal Reserve Bank of Philadelphia, Survey of Professional Forecasters; the Office of Management and Budget; and Wolters Kluwer, Blue Chip Economic Indicators. See www.cbo.gov/publication/61334#data.

The measure of real 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. Positive errors represent overestimates. The diamonds shown on the horizontal axis indicate that the forecast period overlapped a recession by six months or more. (Recessions begin just after a peak in economic activity and run through the subsequent trough.) The years indicate the time span covered by each of the forecast errors shown in the figure.

The second shift occurred in 1997, when growth in labor productivity in the nonfarm business sector accelerated. Such growth then averaged more than 3.0 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 of inflation in the output price index were too high.21 The acceleration in labor productivity stemmed from a pickup in technological progress (especially in 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, for reasons that are not fully understood, the average growth of labor productivity slowed and remained at 1.6 percent per year through 2024. 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 slowed, which in turn slowed the growth of investment and 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.22

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 college 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; petroleum accounts for nearly one-third of the nation’s total energy consumption, as of 2024.23 Crude oil prices are therefore a major component of consumer price inflation; they fluctuate widely in response to economic and geopolitical developments and thus are more volatile than overall prices (see Figure 5).

Figure 5.

Historical Oil Prices

2017 dollars per barrel

Notes

Data source: Congressional Budget Office, using data from the Bureau of Economic Analysis and the Energy Information Administration. See www.cbo.gov/publication/61334#data.

Vertical bars indicate the duration of recessions. Recessions begin just after a peak in economic activity and run through the subsequent trough.

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, rising oil prices in the late 1970s and early 1980s probably contributed to the sizable underprediction of inflation by CBO and the Administration; those forecasters underpredicted the rise in inflation over that period when they completed their forecasts at the end of the 1970s.24 More recently, oil prices increased rapidly in the first half of 2022, which contributed to elevated inflation. CPI inflation was substantially underestimated in all four sets of two-year forecasts made at the beginning of 2021 (see Figure 6).

Figure 6.

Errors in Forecasts of Consumer Price Inflation

Percentage points

Notes

Data Source: Congressional Budget Office, using data from the Bureau of Labor Statistics; the Federal Reserve Bank of Philadelphia, Survey of Professional Forecasters; the Office of Management and Budget; and Wolters Kluwer, Blue Chip Economic Indicators. See www.cbo.gov/publication/61334#data.

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 diamonds shown on the horizontal axis indicate that the forecast period overlapped a recession by six months or more. (Recessions begin just after a peak in economic activity and run through the subsequent trough.) The years indicate the time span covered by each of the forecast errors shown in the figure.

Large changes in crude oil prices reflect producers’ and consumers’ limited capacity to quickly adjust supply and demand in response to changing market conditions.25 Fluctuations in oil prices are often difficult to forecast because markets for petroleum products can be sensitive to rapidly evolving international 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 accurately project CPI inflation.

Until recently, political factors appeared to have waned as a source of uncertainty. Oil prices rose steeply in the lead-up to the 2007–2009 recession and fell sharply in 2015 and 2016 because of shifts in global supply and demand as well as technological changes, such as advancements in horizontal drilling and hydraulic fracturing. However, the war in Ukraine has shown that political factors remain a source of uncertainty. Russia’s invasion of Ukraine contributed to sharp increases in oil prices in 2022. Sanctions levied against Russia, a major petroleum exporter, drove up energy prices in the first half of the year. The duration and severity of the war in Ukraine and further sanctions imposed or enforced against Russia continue to add uncertainty to CBO’s projections of inflation. Ongoing tensions in the Middle East also continue to add uncertainty to CBO’s forecast of inflation, although crude oil prices have continued to moderate since the spike in 2022.

Downward Trend in Interest Rates

Interest rates have trended downward since the early 1980s (see Figure 7). Before the onset of the pandemic, that decline was partly attributable to a lower average rate of inflation. Recent research has identified several additional factors that may have contributed to the decline in real interest rates: the aging of the population, increased income inequality, a trend toward slower output growth, and increased saving among emerging market economies.26

Figure 7.

The Persistent Decline in Interest Rates

Percent

Notes

Data source: The Federal Reserve Board of Governors. See www.cbo.gov/publication/61334#data.

Vertical bars indicate the duration of recessions. Recessions begin just after a peak in economic activity and run through the subsequent trough.

Over the past two decades, forecasters have underestimated the effects of those factors on interest rates, and they did not anticipate the extent or persistence of the eventual decline. Thus, forecasts from all four sources have tended to include sizable overpredictions of both short- and long-term interest rates from the early 2000s until the onset of the 2020 pandemic, when the pattern began to reverse. Since 2020, interest rates have trended upward, leading to underpredictions of both short- and long-term interest rates. Recent patterns of interest rate forecast errors underscore the difficulty of distinguishing transitory movements from long-term changes.27

Decline in the Labor Share of Output

The total compensation paid to workers, measured as a percentage of GDP, is referred to as the labor share. The labor share consists 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 arise from misestimates of wages and salaries.

In the early 2000s, the labor share experienced a structural decline because the growth of labor compensation did not keep pace with output (see Figure 8). Although the reasons for the decline are only partially understood, there are two main theories about it. One is that globalization may have increased incentives for businesses to move their production of labor-intensive goods abroad.28 The second theory is that technological innovation may have increased the returns on capital more than it has increased the returns on labor. At the start of the millennium, forecasters projected that the growth in wages and salaries would continue at its historical average, causing the labor share to stabilize or return to its historical average. That expectation resulted in projections of growth in wages and salaries that were often too high (see Figure 9).

Figure 8.

Labor’s Share of Output

Percent

Notes

Data source: Congressional Budget Office, using data from the Bureau of Economic Analysis. See www.cbo.gov/publication/61334#data.

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. Recessions begin just after a peak in economic activity and run through the subsequent trough.

Figure 9.

Errors in Forecasts of Growth in Wages and Salaries

Percentage points

Notes

Data source: Congressional Budget Office, using data from the Bureau of Economic Analysis and the Office of Management and Budget. See www.cbo.gov/publication/61334#data.

Positive errors represent overestimates. The diamonds shown on the horizontal axis indicate that the forecast period overlapped a recession by six months or more. (Recessions begin just after a peak in economic activity and run through the subsequent trough.) The years indicate the time span covered by each of the forecast errors shown in the figure.

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.29 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 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 the growth of labor compensation and, therefore, the labor share from 1989 to 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 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 average error of CBO’s two-year forecasts of real output growth is −0.1 percentage points if calculated from the most recently available data (see Table 1) but is 0.2 percentage points if calculated from the real-time data available immediately after the conclusion of the two-year horizon. Although some research suggests that real-time data may be more appropriate for that kind of analysis, CBO uses the most recently available data.30 That decision simplifies the analysis and helps account for definitional changes that affect the interpretation of certain data series over time.31

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 grown at a year-over-year rate of 0.7 percent during the third quarter of 2008; however, revised data now show a 0.3 percent increase during that quarter. 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.

Figure 10.

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

Year-over-year percentage change

Notes

Data source: Congressional Budget Office, using data from the Bureau of Economic Analysis. See www.cbo.gov/publication/61334#data.

Real gross domestic product (GDP) is nominal GDP adjusted to remove the effects of inflation.

The vertical bar indicates the duration of a recession. Recessions begin just after a peak in economic activity and run through the subsequent trough.

Effects of the Coronavirus Pandemic

The pandemic significantly affected the economy and therefore many of the economic variables that CBO forecasts. Economic growth and the unemployment rate underwent large swings in 2020 as the economy shut down and reopened. Those swings were unexpected in January 2020, when CBO and others completed forecasts just before the onset of the pandemic. As a result, forecast errors for many of the series that CBO and others project were unusually large in recent years.

The economic effects of the pandemic and the policy responses to them also contributed to a rise in inflation. In early 2021, forecasters were expecting inflation to rise from 1.2 percent in 2020 to roughly 2.1 percent in 2022 (see Figure 11). Actual CPI inflation rose to 4.7 percent in 2021 and 8.0 percent in 2022—a 40-year high. The unexpected increase in inflation was partly caused by factors that were directly related to the pandemic, such as supply-chain disruptions and changes in consumer spending patterns. Policy responses to the pandemic, including federal stimulus payments and the easing of monetary policy, contributed as well by boosting overall demand in the economy. Additional factors unrelated to the pandemic, such as the war in Ukraine and rising food and oil prices, also contributed to rising inflation. Forecast errors for two-year CPI inflation in 2021 were the largest made by CBO, the Administration, the SPF, and the Blue Chip consensus over the 1982–2021 sample period common to all four sources.32

Figure 11.

Recent Forecasts of Consumer Price Inflation

Year-over-year percentage change

Notes

Data source: Congressional Budget Office, using data from the Bureau of Labor Statistics; the Federal Reserve Bank of Philadelphia, Survey of Professional Forecasters; the Office of Management and Budget; and Wolters Kluwer, Blue Chip Economic Indicators. See www.cbo.gov/publication/61334#data.

The forecasts are of inflation in the consumer price index for all urban consumers.

The forecast errors for the five-year inflation forecasts completed in 2019 and 2020 were also affected by the rise in inflation in 2021 and 2022. Forecasters expected CPI inflation to average between 2.1 percent and 2.5 percent for the 2019–2023 and 2020–2024 periods (see Table 3). Actual CPI inflation averaged 3.9 percent over the 2019–2023 period and 4.2 percent over the 2020–2024 period.

The unexpectedly high inflation in 2021 and 2022 affected the forecast errors for several other series that are analyzed in this report. For example, in response to higher inflation, the Federal Reserve raised interest rates more than forecasters had expected in early 2021. Wages and salaries were also higher than anticipated because nominal wages and salaries increased at a faster pace as a result of unexpectedly tight labor market conditions in 2021 and 2022.

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

This appendix provides an overview of the forecast and historical data that the Congressional Budget Office uses to evaluate its economic 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 mainly compiled from four sources: CBO’s annual report, The Budget and Economic Outlook, typically published in the first quarter of each calendar year; the Administration’s annual budget documents; reports by the Survey of Professional Forecasters (SPF) published during the first quarter of each calendar year; and Blue Chip Economic Indicators reports from the first quarter of each calendar year. Forecasts published in CBO’s annual report, The Budget and Economic Outlook, are typically finalized about two months beforehand, unlike the private-sector forecasts included in this analysis. 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 from 1976—the first year that CBO made economic projections—to 2023. From 1976 to 1984, however, CBO did not regularly publish its 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 from 1976 to 2023, 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 analyzes only 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. Therefore, 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, this report’s comparisons of forecast quality do not include CBO’s forecasts produced from 1976 through 1978 (see Table 2).

The SPF was published beginning in 1968 and was first conducted by the American Statistical Association and the National Bureau of Economic Research. The Federal Reserve Bank of Philadelphia began conducting the survey in 1990. Because the availability of data is limited, this report analyzes only SPF forecasts dating back to 1982. Most of the SPF forecasts used in this analysis were issued in February of the initial forecast year, with a few exceptions. One such exception is the two-year forecasts published in 2019, which were issued in March because of a federal government shutdown. The data are averages of responses from the 30 to 40 panelists who participate in the survey each quarter. Additionally, the SPF does not publish five-year forecasts for most variables included in this report, thus limiting comparisons with CBO’s forecasts to a two-year horizon (see Table 1). Because the SPF’s two-year forecasts are only available starting in 1982, this report’s comparisons of two-year forecast quality do not include CBO’s forecasts produced from 1976 to 1981.

The Federal Reserve’s forecasts are taken from two sources. The first is the Federal Reserve’s Tealbook (formerly Greenbook), a publication prepared by the staff of its Board of Governors. The Tealbook 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 incorporates only Tealbook forecasts from 1979 to 2019. 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 (which exclude the three highest and three lowest individual projections) of those forecasts encompass a smaller set of variables, but they have been published regularly since 2007. In this analysis, CBO uses the median of the FOMC’s forecasts from 2020 to 2023.1 All 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 output (that is, output adjusted to remove the effects of inflation) 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 five-year time horizons, CBO computes the geometric average of each series over the relevant sample period.2

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).3 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 from 1992 to 2023.

Unemployment

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 horizons, CBO computes the average 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 on the old basis for forecasts from 1976 to 1994 and relative to the new basis for forecasts made from 1995 to 2023.

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 consumer price index for all urban consumers) in addition to the CPI-W (the consumer 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 actual amounts and CPI-W forecasts with CPI-W actual amounts.

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, the SPF, and the Federal Reserve have always forecast the CPI-U. The SPF computes annual forecasts of inflation in the CPI-U as a fourth-quarter over fourth-quarter percentage change, unlike CBO, the Administration, or the Blue Chip consensus.

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 from 1992 to 2023. CBO subtracts growth in the output price index from growth in the CPI 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 arithmetic average of the annual interest rate series over the relevant sample period. The agency’s analysis of forecasts 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.

CBO and the SPF have always forecast the secondary-market rate. The Administration published forecasts of the new-issue rate until 2001, when it switched to forecasting 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 subtracts each forecaster’s annualized projections of CPI inflation from that forecaster’s annualized projections of nominal interest rates. Although the forecasters used different measures of inflation and short-term interest rates, the resulting forecasts of real interest rates 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 arithmetic 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. The return on 10-year Treasury notes is guaranteed by the federal government, whereas 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. The SPF forecast only the Aaa corporate bond rate until 1992, when it began to forecast both the Aaa corporate bond rate and 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 neither the Blue Chip Economic Indicators report nor the SPF report include a consensus forecast of the growth in wages and salaries, they are 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 from 1976 to 1991 and nominal GDP for forecasts made from 1992 to 2021. 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 horizons.

About This Document

The Congressional Budget Office regularly evaluates the quality of its economic forecasts by comparing them with the economy’s actual performance and with the forecasts of other institutions. Such evaluations help guide CBO’s efforts to improve the quality of its forecasts and can assist Members of Congress in their use and understanding of the agency’s estimates. In keeping with CBO’s mandate to provide objective, impartial analysis, the report makes no recommendations.

Chandler Lester and Natalia Reyes prepared the report with guidance from Robert Arnold (formerly of CBO), Devrim Demirel, Daniel Fried, Edward Gamber (formerly of CBO), and Jaeger Nelson. Christina Hawley Anthony, John McClelland, and Robert Sunshine (a consultant to CBO) offered comments. Nicholas Abushacra (formerly of CBO) and Griffin Young fact-checked the report.

Jeffrey Kling reviewed the report, and Scott Craver edited it. Casey Labrack created the graphics and, with assistance from Jorge Salazar, prepared the text for publication. The report is available at www.cbo.gov/publication/61334.

CBO seeks feedback to make its work as useful as possible. Please send comments to communications@cbo.gov.

Phillip L. Swagel

Director

July 2025