Office of Economic Policy

Working Paper 2021-02

January 2021

The Expectations Gap

Alexander D. Schibuola and Andrew B. Martinez

This Office of Economic Policy Working Paper presents original research by the staff of the Office of Economic

Policy. It is intended to generate discussion and critical comment while informing and improving the quality

of the analysis conducted by the Office. The paper is a work in progress and subject to revision. Views and

opinions expressed are those of the authors and do not necessarily represent official Treasury positions or

policy. Comments are welcome, as are suggestions for improvements, and should be directed to the authors.

This Working Paper may be quoted without additional permission.
The Expectations Gap

Alexander D. Schibuola* and Andrew B. Martinez† 

January 14, 2021 

Abstract

The output gap hinges on estimates of potential GDP, which can vary widely and are ultimately 

unobservable. Beckworth (2020) circumvents this using historical surveys of professional 

forecasters’ nominal GDP projections to construct an “expectations gap,” which shifts the 

benchmark for economic performance to what GDP was expected to be. However, this approach 

has some limitations, which we address as follows: (1) we assess the sensitivity of the expectations 

gap to alternative surveys of professional forecasters and different forecast horizons; (2) we use 

forecaster disagreement to capture the uncertainty surrounding the expectations gap; (3) we 

estimate both the nominal and real expectations gaps and compare them against other conventional 

output gap measures; (4) we produce real-time forward-looking estimates of the expectations gap 

and assess their performance near business cycle turning points. Though the expectations gap is 

closely correlated with existing output gap measures, we find that several historical episodes 

suggest that it contains additional useful information about the state of the economy. 

Keywords: Business Cycles; Financial Stability; Forecast Accuracy; GDP Forecasts

JEL classifications: C18, E32, E37 

*Office of Macroeconomic Analysis, U.S. Department of the Treasury. The views expressed here 

are those of the authors and do not necessarily represent the views of the Treasury Department 

or the U.S. Government. Thanks to David Beckworth, Stephen Miran, and Tara Sinclair for 

helpful comments and discussions. Contact: Alexander.Schibuola@treasury.gov. 

†Office of Macroeconomic Analysis, U.S. Department of the Treasury; H.O. Stekler Research 

Program on Forecasting, The George Washington University; Climate Econometrics at Nuffield 

College. Contact: Andrew.Martinez@treasury.gov.
1 

Navigating by the stars can sound straightforward. Guiding policy by the stars in 

practice, however, has been quite challenging of late because our best 

assessments of the location of the stars have been changing significantly.

Federal Reserve Chair Jerome Powell, August 18, 2018

1 Introduction 

The output gap – the percentage difference between actual GDP and potential GDP – is used to 

forecast inflation and inform economic policy.1 However, muted inflationary pressures and the 

ascendance of financial stability concerns, along with the unobservability, sizeable revisions, and 

variety of potential GDP estimates, have reduced the reliability of output gaps as a measure of 

economic slack.2 Potential GDP can be estimated using production functions,3 structural and 

statistical approaches,4 or assorted variations of the two.5 Accordingly, different models produce 

very different estimates. For example, between 2012 and 2017, the Congressional Budget Office 

(CBO) reported a negative output gap, while the Laubach and Williams (2003) methodology 

consistently reported a positive output gap. 

More recently, Beckworth (2020) calculates economic slack as the gap between nominal and 

“neutral” GDP. He estimates neutral GDP by averaging five years of professional forecasts for a 

particular quarter’s nominal GDP.6

 Since professional forecasts are measured by the central 

tendency from surveys, they do not depend on any single model and reflect what actual 

expectations were at the time. This shifts the emphasis from “navigating by the stars” to 

navigating by what agents expect. 

However, this “expectations gap” has several limitations. First, its sensitivity to alternative 

forecaster surveys and forecast horizons is unclear. Second, it is difficult to assess the strength of 

its signal about the economy since there are no measures of uncertainty. Third, estimation of the 

expectations gap in nominal terms complicates a direct comparison with conventional output gap 

measures, which are typically formulated in real terms. Fourth, it does not fully leverage the 

availability of real-time data to assess the performance near business cycle turning points. 

We make the following contributions: First, we assess the sensitivity of the expectations gap to 

different surveys of professional forecasters and alternative forecast horizons. Second, we 

estimate the uncertainty surrounding the expectations gap by incorporating individual forecaster 

disagreement. Third, we evaluate how the nominal and real expectations gaps compare against 

the nominal and real output gaps produced by the CBO, Quast and Wolters (2020), and the 

1

 E.g., the Taylor rule, see Taylor (1993), the output gap has also been considered for fiscal policy rules; see Taylor 

(2000) and Kumhof and Laxton (2013). 

2

 See Powell (2020): “Before the Great Moderation, expansions typically ended in overheating and rising inflation. 

Since then ... a series of historically long expansions had been more likely to end with episodes of financial 

instability...” Federal Reserve Chair Jerome Powell, August 27, 2020. 

3

 See Shackleton (2018). 

4

 See Fleischman and Roberts (2011) and Berge (2020). 

5

 See Ball and Mankiw (2002), Laubach and Williams (2003) and Williams et. al. (2020), Coibion et. al. (2018). 6

 This is a refinement of one of the approaches evaluated in Coibion et. al. (2018). 
2 

nominal and real expectations gaps derived from the Federal Reserve Board’s Greenbook 

forecasts. Fourth, we construct real-time and forward-looking estimates of the expectations gap 

and evaluate their performance, especially around business cycle turning points. 

Our findings are as follows. First, the use of alternative professional forecaster surveys does not 

substantively alter expectations gap estimates. The lack of sensitivity to any particular survey 

suggests it is a robust economic indicator. However, the expectations gap measures are sensitive 

to the selection of forecast weighting and forecast horizon. Second, our analysis of forecaster 

disagreement shows that there is considerable uncertainty surrounding the expectations gap, 

which is consistent with traditional measures of the output gap; see Berge (2020). Despite the 

uncertainty, the expectations gap unambiguously signals economic overheating or 

underperformance in four distinct episodes. Third, we find meaningful differences between the 

nominal and real expectations gaps. This sharply contrasts with the CBO’s nominal and real 

output gaps that are constructed to be identical. For example, during the mid-2000s when home 

values were judged to be overvalued, the real expectations gap and the CBO output gap indicate 

little-to-no economic overheating, while the nominal expectations gap does. Fourth, our real-time 

analysis of the expectations gap shows that significant revisions occur – mostly due to 

comprehensive BEA updates. However, these revisions are comparable in size to those made to 

CBO’s output gap. Lastly, the forward-looking estimates outperform simple benchmarks and 

occasionally provide a leading indicator of business cycle turning points. 

The next section describes the methodology and intuition of neutral GDP and the expectations 

gap. Section 3 estimates the expectations gap and explores the sensitivity to alternative surveys, 

forecast horizons, and forecaster disagreement. Section 4 compares the nominal and real 

expectations gaps with other output gap estimates. Section 5 derives real-time, forward-looking 

expectations gaps and evaluates their performance around business cycle turning points. Section 

6 concludes.

2 Background and Methodology 

This section explains the underlying intuition and estimation of neutral GDP. While conventional 

output gap measures are the difference between actual and potential GDP, the expectations gap 

measures the difference between actual and neutral GDP. Beckworth (2020) defines neutral 

GDP as “the public’s expected growth path of nominal income.” If actual GDP equates to neutral 

GDP, then, on average, households’ and firms’ expectations are validated by outcomes. Diagram 

1 illustrates the key differences between the formulation of conventional output gaps and the 

expectations gap. 

A key rationale of the expectations gap is that deviations of income from expectations that 

informed past adoption of debt obligations alters debt burdens, which can impinge on current and 

future spending decisions. Thus, while the conventional output gap measured in inflationadjusted terms is geared towards predicting inflation vis-à-vis a Phillips curve, the expectations 

gap in nominal terms is more focused on risks to financial stability. Greater-than-expected 

income growth can lead to greater debt accumulation and leave agents more susceptible to 

negative shocks when lower-than-expected income episodes arise. 
3 

Diagram 1: Schematic Illustrating Different Components of Gap Measures

2.1 Intuition and Simplifying Assumptions 

A household’s expected income stream influences its spending decisions.7 If, for example, a 

household expects an annual income of $60,000 indefinitely and finances the purchase of a home 

through a 30-year mortgage amortized by fixed payments of $2,000 per month, then 40 percent 

of the household’s monthly income is tied up by this fixed debt obligation. If an economy-wide 

shock leads the household’s realized income to fall 10 percent below expectations, then its 

monthly mortgage payments will encumber 44.4 percent of its income. This forces the household 

to reduce current spending, savings, or both, until the debt is paid off or restructured.8

 The 

deviation of actual outcomes from past expectations can influence both current and future 

spending via sticky nominal debt obligations. Analogously, a firm’s allocation of resources can 

be affected when actual revenues deviate from expectations. The incorporation of these 

dynamics into macroeconomic models suggests sticky debt burdens play a more important role 

than sticky prices; e.g. see Sheedy (2014) and DiCecio and Bullard (2019). 

Aggregated household income expectations or aggregated business revenue expectations 

represent the “neutral” level of income in the economy wherein expectations, in the aggregate, 

are consistent such that past spending and borrowing decisions prove to have been ideal. Since 

aggregate private agents’ individual income projections do not exist, Beckworth (2020) uses the 

GDP projections from surveys of professional forecasters as a proxy to estimate neutral income.9

7

 E.g. see Romer (1990) for an argument of how collapsing future income expectations led to declines in purchases 

of durable goods during the Great Depression. 

8

 This may especially be the case if the income loss is expected to be permanent. 9

 This equates aggregate income, spending, and output with GDP, which implies that changes in business inventories 

are irrelevant. This should be satisfied when (1) the expected contribution to GDP growth from business inventories 
4 

Though some households and firms may fare better or worse than expected, if actual GDP 

remains near the neutral level, these differences cancel out. In contrast, if actual GDP falls short 

of neutral GDP, this signifies that households’ and firms’ expectations were, on average, overly 

optimistic. 

2.2 Description of Survey Forecasts 

The Survey of Professional Forecasts (SPF) is a quarterly survey of about 40 mostly academic 

and business forecasters. It was first published in Q4-1968 as the ASA/NBER Economic Outlook 

Survey. Management of the survey was transferred to the Federal Reserve Bank of Philadelphia 

in 1990. In total, 443 unique forecasters have participated in the SPF since 1968. 

The SPF is published near the middle of the second month of every quarter. Forecasts are 

reported for fixed-horizons, extending four-quarters ahead. In 1990, the SPF began including a 

forecast of the average annual real GDP growth rate over the next 10 years and the average 

annual CPI inflation rate over the next 10 years.10 While the latter is updated with every quarter, 

the longer-run real GDP forecast is updated only in the first SPF survey of every year. 

The Blue Chip Economic Indicators (BCEI) is a monthly survey of about 50 mostly business 

forecasters maintained by Wolters Kluwer that began in 1976. More than 115 unique forecasters 

have participated in the survey since 1980. 

The survey is published on the tenth business day of every month with quarterly fixed-event 

forecasts that shrink from eight- to four-quarters ahead as a given year progresses (e.g., the 

monthly short-run forecast surveys for 2020 have a fixed endpoint of Q4-2021). Longer-run 

forecasts are updated twice per year in the March and October surveys. Initially only the 2-6 and 

6-11 year average forecasts of variables were available. Since October 1982, the surveys have 

included annual growth forecasts for two-to-six years ahead and a five-year average forecast for 

6-11 years ahead. Prior to 1990, only the average – “consensus forecast” – of variables was 

reported, however, since then, the average forecasts of the top 10 and bottom 10 forecasters have 

also been reported.

2.3 Estimating Neutral GDP and the Expectations Gap 

Following Beckworth (2020), the neutral level of GDP at time t is measured as the average of 

the forecasts of time period t’s GDP submitted over the past 20 quarters, including the nowcast: 

Yഥ௧,௩ ൌ 1

20෍ Y௧|௧ି௛,௩, ሺ1ሻ

ଵଽ

௛ୀ଴

is minimal over longer forecast horizons and (2) the average historical contribution of changes in business 

inventories to GDP growth is 0.0 percent. 

10 The SPF and BCEI surveys prior to 1992 reported forecasts for GNP rather than GDP. We refer to GNP as GDP 

where applicable for simplicity. 
5 

where Y௧|௧ି௛,௩ is the aggregate forecast of GDP at time t and data vintage v based on expectations 

at time tെh. The aggregate forecast represents the central tendency of professional forecasters’ 

expectations for the level of GDP at time t. Beckworth (2020) uses the SPF-reported median 

while we focus instead on the BCEI-reported consensus (i.e. average) forecast.11

This formulation does not impose restrictions on how forecasts are generated, i.e. it is not 

dependent on any one method or model. Quast and Wolters (2020) use a modified-Hamilton 

filter to estimate potential GDP. It is a special case in which Y௧|௧ି௛,௩ represents the forecasts from 

an autoregressive model with four-quarter lags. This provides a statistical justification for 

equation (1). It also implies that neutral and potential GDP estimates would be aligned if all 

forecasters used this model.12 However, as the expected path of actual GDP can deviate from 

potential GDP, especially in the short-run, then measures of neutral GDP will differ from 

estimates of potential GDP. 

Another important difference between the approach by Quast and Wolters (2020) and the 

expectations gap is the choice of forecast horizons. Neutral GDP estimates are centered on 10-

quarters-ahead forecast with horizons between zero to 20-quarters ahead. This is considerably 

wider than the Quast and Wolters (2020) potential GDP estimates that are centered on the eightquarters-ahead forecast with horizons ranging from four- to 12-quarters ahead. However, Quast 

and Wolters (2020) note that centering estimates around the 10-quarters-ahead forecast 

effectively allows for the inclusion of longer financial and credit cycles into the standard 

business cycle; e.g., see Beaudry et. al. (2020). This is consistent with the intuition underpinning 

the calculation of neutral GDP. The inclusion of longer horizons captures the rigidities stemming 

from fixed nominal debt obligations, which are assumed to be “sticky” for five years. 

In practice, the GDP-level forecast for a particular horizon is not always available, and when it 

is, it is associated with a particular data vintage. Following Beckworth (2020), we construct the 

forecast in levels based on the underlying GDP growth rate forecasts so that 

Y௧|௧ି௛,௩ ൌ ෑ൫1 ൅ g௧ି௛ା௝|௧ି௛ିଵ൯

௛

௝ୀ଴

Y௧ି௛ିଵ,௩, ሺ2ሻ 

where gtെh൅j|tെh is the expected quarterly GDP growth rate for period t that is formulated at 

time tെh, and Y௧ି௛ିଵ,௩ is the initial level of GDP. 

It is possible to obtain the quarterly-growth rate forecasts directly from forecaster surveys for up 

to four-quarters ahead for the SPF and between four- to eight-quarters ahead for the BCEI. To 

extend the SPF forecasts out 5-year ahead beyond the fixed horizon of four quarters, Beckworth 

11 Although the BCEI forecasts are released monthly, for simplicity we do not change the notation. Since three 

months of BCEI forecasts are released in a given quarter, they can be converted from monthly to quarterly by 

averaging the monthly forecasts. 

12 Quast and Wolters (2020) focus on the log of the level of GDP whereas here we emphasize the level of GDP so 

that this special case requires that the exponential of the AR(4) model forecasts of the log level are a good 

approximation of forecasts of the level of GDP. Depending on how forecasters generate their expectations, the 

estimate of the neutral level of GDP could also align with other estimates of potential GDP. 
6 

(2020) uses the latest 10-year real GDP growth rate forecast as a proxy for growth rates beyond 

four-quarters ahead. For the BCEI, we use the latest long-run annual growth rate forecasts to 

forecast GDP out to five-years ahead. 

While the neutral level of GDP can be constructed in either nominal or real terms, we focus on 

the nominal measure following Beckworth (2020). While short-run nominal GDP growth and 

level forecasts are available from both SPF and BCEI, the SPF only produces real long-run 

growth forecasts. Therefore, Beckworth (2020) constructs a proxy long-run nominal GDP 

growth forecast based on the long-run real GDP growth forecast and the CPI inflation forecast. 

Beckworth (2020) also adjusts the CPI forecast to conform to the GDP deflator according to the 

historical wedge between short-run forecasts of CPI and the GDP deflator. 

The choice of the initial value of output, i.e. Y௧ି௛,௩, is important for ensuring that a consistent 

GDP-level is applied over time. For example, Beckworth (2020) focuses primarily on the latest 

vintage of GDP data, vൌT. However, since real-time estimates are subject to substantial 

revisions over time, they may exhibit additional volatility beyond the underlying business cycle 

fluctuations; see Orphanides and Norden (2002). We explore this in Section 5. 

Lastly, the expectations gap is constructed as the percentage difference between the observed 

level of GDP at time t and the estimate for neutral GDP at time t

 GAP௧,௩ ൌ ቆY௧,௩ െ Yത

௧,௩

Yത

௧,௩

ቇ ∗ 100, ሺ3ሻ

where the choice of data vintage – e.g., first estimate or latest estimate of GDP – can affect the 

expectations gap estimates; see Sinclair and Stekler (2013). 

3 The Sensitivity of the Expectations Gap 

This section evaluates the sensitivity of the nominal expectations gap along three dimensions: 

First, the sensitivity to the choice of forecaster survey; second, varying the forecast horizons; and 

third, accounting for forecaster disagreement. Ultimately, regardless of whether the SPF or BCEI 

survey is used, the expectations gap estimates exhibit little difference. In contrast, expectations 

gap estimates are sensitive to the selected forecast horizons and the weights assigned to 

forecasts, particularly at longer horizons. Forecaster disagreement quantifies the range of 

uncertainty surrounding the expectations gap and highlights the four distinct episodes in which 

the expectations gap gave an unambiguous signal about the state of the economy. 

3.1 Alternative Forecaster Surveys 

We start by calculating the expectations gap from Q1-1985 to Q3-2020 using the BCEI and SPF 

forecasts. To extend the latter back to Q1-1985, when long-run forecasts were not available we 

use the last published growth rate forecast for the four quarters beyond the current quarter as a 

proxy for the long-run growth rate up to five-years ahead (this is referred to as “SPF (extended)” 

in Figure 1). 
7 

Except for minor deviations around 1991-97, Figure 1 illustrates that the BCEI and SPF nominal 

expectations gaps are very similar, which implies that they capture features of the economic 

cycle rather than features specific to the surveys. Given the minor differences between the SPF 

and BCEI measures, the remaining analysis in this paper focuses on the BCEI measure. 

Figure 1: Measures of the Nominal Expectations Gap (Q1-1985 – Q3-2020) 

Figure 1 also illustrates that using the one-year-ahead growth rate forecasts as a proxy for longerhorizon SPF forecasts is tenable since there are no large discrepancies from the BCEI-derived 

measure between 1985 and 1990. Thus, it is possible to extend the SPF-derived expectations gap 

measure back to the early 1970s using this approach. 

3.2 Alternative Forecast Horizons 

Isiklar and Lahiri (2007) show that professional forecasters can capture both the trend and cycle 

in economic growth up to one year ahead. Therefore, including these short-term forecasts within 

the measure of neutral GDP may produce a more muted expectations gap. By removing the 

short-term forecasts from the estimates of neutral GDP, we can observe the consequent impact 

on the expectations gap. 

In Panel A of Figure 2 lines go from lighter to darker as more short-term GDP-level forecasts are 

excluded from the estimates of neutral. The removal of short-term forecast horizons from the 

calculation of neutral GDP puts greater weight on the more distant forecasts of GDP made for a 

particular quarter. Differences tend to arise around peaks and troughs in the expectations gap 

series. This is especially pronounced following the trough in the expectations gap in 1985-88 and 

in 2009-12 where removing the short-term forecasts exacerbates the gap. 

Conversely, removing forecasts made in the more distant past from a particular quarter’s neutral 

GDP estimates signifies that debts are less sticky. At the limit, were neutral GDP derived solely 

from the nowcast, it would imply that debt obligations are fully flexible and are adjusted from 

quarter-to-quarter so as to leave the distribution of debt burdens unchanged. Panel B of Figure 2 
8 

shows the consequences of removing the more distant forecasts and depicts a faster convergence 

of the expectations gap to zero. 

Figure 2: Sensitivity of the Nominal Expectations Gap to Alternative Horizons 

Figure 2 illustrates that the expectations gap – and the amount of economic slack it implies –

depends on which level forecasts are included. Averaging five years of forecasts to estimate a 

quarter’s level of neutral GDP is a plausible starting point if the average debt contract is assumed 

to be refinanced within five years from the contract date. However, if leverage is driven at times 

by an increase in shorter-term debt contracts, then the five-year average may artificially 

exacerbate the cyclicality of the expectations gap. This is especially pertinent if long-term debt 

obligations are less likely to be contracted during periods of high and volatile inflation. 

In addition to the optimal range of forecast horizons, the equal weighting scheme for each 

forecast horizon merits consideration, as this assumes debts are contracted continuously and 

uniformly over time. Debts are accrued at different rates across the business cycle such that more 

lending takes place during an expansion than a recession. To better align neutral GDP estimates 

with actual debt burdens, then forecasts made during a recession should receive less weight than 

those made during an expansion. Future research should endogenize the optimal range and 

weighting of forecasts to vary over time and in response to economic variables, such as when 

debt burdens are accrued and the debt maturity structure. 

Another concern is that many of the forecasts used to estimate the neutral level of GDP are 

derived from less frequently updated longer-term growth rate projections. Thus, the actual 

forecast path of GDP over longer-horizons may be more variable than the intermittently updated 

longer-run forecasts permit. However, this issue may be mitigated since the longer-run forecast 

GDP level path is heavily influenced by the short-term forecasts that are updated with every 

survey release. This is demonstrated by the SPF (extended) measure in Figure 1, which is 

constructed by extrapolating the one-year ahead growth rate forecast out over five years, very 

closely tracking the measures based on the less frequently updated long-term forecasts. 
9 

3.3 Measuring Uncertainty through Forecaster Disagreement 

The expectations gap uses measures of central tendency to construct the neutral level of GDP, 

which ignores disagreements between individual forecasters that can be persistent and linked to 

the business cycle; see Patton and Timmermann (2010) and Bürgi and Sinclair (2020). A large 

dispersion around the central tendency indicates that aggregate expectations about the neutral 

level may be more uncertain. To quantify this uncertainty, we estimate the forecaster 

disagreement around the expectations gap. 

There are two different ways to measure of forecaster disagreement: disagreement between 

individual forecaster measures of the neutral level and disagreement between the underlying 

forecasts of GDP. Adapting equation (1) to allow for individual forecasters allows us to focus on 

the first source: 

 Yഥ௜,௧,௩ ൌ 1

20෍ Y௜,௧|௧ି௛,௩, ሺ4ሻ

ଵଽ

௛ୀ଴

where the index i represents an individual forecaster. This illustrates that a measure of the neutral 

level for each individual forecaster can be constructed at each point in time. However, to do so, 

forecasters must have generated at least one forecast for time period t per quarter for 20 

consecutive quarters (i.e., a forecaster is in the sample continuously for five years). In practice 

we relax this restriction by allowing forecasters to miss up to three consecutive surveys and 

interpolate between the missing forecasts to ensure retention of a sufficiently large sample over 

time. 

While the BCEI does not release individual forecasts publicly, the SPF does. In total, 79 unique 

SPF forecasters satisfy the requirements (out of the full sample of 186 since 1990). We construct 

an expectations gap for each of these forecasters and then calculate the minimum and maximum 

individual expectations gaps at each point in time. This captures the full range of disagreement 

between forecasters and measures the uncertainty around aggregate expectations at each point in 

time; see Lahiri and Sheng (2010). 

Panel A of Figure 3 plots this range (in purple) along with the median derived from the 

individual measures. The median of the individual expectation gaps is very similar to the 

expectations gap constructed from the median forecaster. This suggests that focusing on the 

subset of 186 forecasters does not bias our analysis. The dispersion between individual 

expectation gaps is fairly stable over time, ranging approximately three to four percentage points 

with an increase during the dotcom boom in 2000 and a prolonged increase during the housing 

boom and bust in 2005-10. Only in recent years has it gradually declined. 

An alternative approach for measuring forecaster disagreement is to consider the forecasts from 

the top or bottom 10 forecasters at each point in time. This is beneficial because it does not rely 

on individual expectations gaps with ranges that could be biased by the entry and exit of 

individual forecasters over time. Conversely, it is not necessarily representative of any individual 

forecaster’s views since the top / bottom forecasters vary across variables, horizons, and time. It 
10 

also does not capture the full range of forecaster dispersion, but each horizon corresponds 

analogously to a plus / minus one standard deviation in forecaster disagreement.13

Figure 3: Incorporating Forecaster Disagreement 

Panel B of Figure 3 plots the top / bottom 10 forecaster measures of the expectations gap for the 

BCEI while Panel A plots it for the SPF (in red). The dispersion of the BCEI forecasters is larger 

than the SPF (7 percentage points vs. 4 percentage points), indicating that individual 

expectations in the BCEI are more diffuse than those in the SPF. 

Both measures of forecaster disagreement illustrate that there are four episodes during which the 

economy clearly performed significantly above or below most individual forecasters’ 

expectations. The first episode corresponds with the dot-com boom when the economy grew 

faster than expected; the second episode was during and after the 2008-09 recession when the 

economy grew much slower than expected; the third episode was during the so-called “invisible 

recession” 14 of 2015-16; and the fourth episode occurred during the COVID-19 pandemic 

induced recession in 2020. Each of these episodes corresponds to important turning points in the 

economy and so support the interpretation of the expectations gap as an indicator of business 

cycle turning points. 

4 Comparing Nominal and Real Gaps 

While this paper focuses on the nominal expectations gap, this section applies the same 

methodology from Section 3 to produce a real expectations gap from real GDP data and BCEI 

forecasts. Despite a high degree of correlation, the nominal expectations gaps appears to suggest 

additional useful information about the state of the economy. 

13 Reifenschneider and Tulip (2019) use a similar approach to calculate forecast uncertainty. 14 See Neil Irwin in 2018: https://www.nytimes.com/2018/09/29/upshot/mini-recession-2016-little-known-bigimpact.html, last accessed 17 September 2020. 
11 

We also compare the BCEI-based expectations gap with three alternative gap measures: the realtime output gaps from the Federal Reserve Board’s Greenbook15 staff forecasts and the nominal 

and real expectations gap estimates that we derive from them; the CBO’s output gap; and the 

output gap generated using the modified Hamilton filter of Quast and Wolters (2020). There are 

similarities between measures, however, allowing for nominal fluctuations produces a more 

holistic measure of the economic cycle. 

Panel A of Figure 4 plots the nominal and real expectations gaps derived from the BCEI surveys. 

From the mid-to-late 1980s the real gap was positive while the nominal gap was negative, which 

is consistent with the disinflation of that period. The differences between the real and nominal 

gaps was broadly stable throughout the 1990s even as both measures increased. Since the 2001 

recession, the nominal and real measures have followed broadly similar patterns except for two 

episodes. First, during the mid-2000s, when home prices and construction were rapidly rising, 

the nominal gap indicates overheating whereas the real gap does not. The second episode occurs 

in 2015-16, when the nominal gap drops more sharply than the real gap, thus better capturing the 

“invisible recession” that was associated with declines in energy, agriculture, and manufacturing 

activity. These episodes suggest the nominal gap is more informative for business cycle analyses. 

A comparison of the nominal and real expectations gap estimates with conventional output gap 

measures is complicated since they are derived from different sources. To provide a more 

“apples-to-apples” comparison of the expectations and output gaps, we construct the nominal 

and real expectations gaps from the Federal Reserve Board staff’s Greenbook forecasts and 

compare these to their output gap forecasts.16

Panel B of Figure 4 plots all three measures up through 2015, which corresponds to the last 

publicly accessible Greenbook. Moreover, the Greenbook output gap was only released 

beginning in 1987. The Greenbook expectations gap estimates are similar to the BCEI 

expectation gaps. Notably, the nominal expectations gap is more closely correlated to the realtime output gap (0.92) than the real expectations gap (0.81). The Greenbook output gap shows a 

larger decline following the 1990-91 recession but suggests less overheating during the late 

1990s and more quickly turns negative in 2000. It exhibits much less overheating in the mid2000s than the nominal expectations gap but tracks both expectations gaps closely after 2008. 

Overall, the expectations gap for an individual forecaster can be thought of as a proxy for the 

implicit output gap used to construct the forecasts. 

To explore how sensitive measures of the output gap are to being conveyed in nominal or real 

terms, we look at two alternative approaches for constructing the output gap. The first, shown in 

Panel C, is the latest measure by CBO that uses a production-function approach to estimate 

potential GDP and assumes the real and nominal measures of the output gap are identical. The 

measures do not indicate any overheating associated with the housing price boom in the mid-tolate 2000s nor do they capture the slowdown in 2015-16. 

15 Since mid-2010 the Greenbook has been replaced by the Tealbook. 16 While the Greenbook does not explicitly contain long-term forecasts, we extrapolate long-term forecasts out fiveyears ahead by using the last quarterly growth rate forecast (typically between four- to eight-quarters ahead) as a 

proxy for the long-run growth rate. 
12 

Another statistical approach is the real-time measure by Quast and Wolters (2020) that employs a 

modified Hamilton filter to estimate potential GDP, which we replicate using both real and 

nominal GDP. The resulting output gap estimates are plotted in Panel D of Figure 4. Unlike the 

CBO, the nominal and real measures have large and persistent differences. While the real output 

gap remains positive with only occasional dips into negative territory around recessions, the 

nominal output is almost always negative except for at the peak of a cycle. Both versions suggest 

the economy was overheating during the mid-2000s but diverge in 2015-16 with the real output 

gap rising and the nominal output gap falling sharply. 

Figure 4: Real vs. Nominal Gaps 

Our analysis suggests that the real and nominal gaps can convey different information. While the 

CBO assumes away these differences, the episodes in the mid-2000s and 2015-16 suggest that 

including nominal fluctuations provides a clearer picture of overheating and underperformance 

associated with episodes of broader price changes. Conversely, both the real expectations gap 

and conventional output gaps missed or downplayed these economic developments. Thus, the 

nominal expectations gap is more informative for analyses of business cycles. 
13 

However, there is a notable oddity in both the nominal and real expectations gap around the 2001 

recession: these do not turn negative until the end of that recession. A positive expectations gap 

during a recession seems contradictory. It implies that actual GDP is still greater than expected, 

on average. A potential explanation is that more debts were accrued during the dotcom boom 

leading up to the 2001 recession.17 In more distant forecasts prior to the dotcom boom are 

excluded then the gap turns negative more quickly. This interpretation is corroborated by Panel 

B of Figure 2, which shows that the expectations gap turns negative earlier near the 2001 

recession when more distant forecasts are excluded from the estimation of neutral GDP and the 

expectations gap. 

5 Real-Time and Forward-Looking Estimates 

This section shows (1) how the expectations gap changes between first, second, third, and the 

latest estimates of GDP, (2) how a forward-looking expectations gap measure can be derived, 

and (3) how forward-looking gap estimates perform near business cycle turning points. The realtime exercise illustrates the measures that would have been observable at given moments of time. 

This section focuses exclusively on nominal measures. Therefore, any mention of the 

expectations gap, GDP, or neutral GDP refers to the nominal measures thereof. 

5.1 Real-Time Estimates 

As outlined in Section 2, forecasted GDP growth rates are combined with actual GDP levels to 

generate forecasts for the level of GDP. The average of these forecasts over a 5-year horizon 

yields a neutral GDP estimate, and the percentage difference between actual GDP and neutral 

yields the expectations gap. However, the BEA revises GDP data over time and this can lead to 

sizeable changes in the estimate expectations gap. 

Estimating a real-time expectations gap involves taking the percentage difference between the 

real-time estimate of GDP and the real-time estimate of neutral GDP. The first aspect is simple 

as historical vintages of GDP estimates are readily available; see Croushore and Stark (2001). 

For example, for Q1-1997 the first, second, and third estimates of that quarter’s GDP were 

released in April, May, and June 1997. 

The second aspect, estimation of real-time neutral GDP, is more intensive. Each monthly vintage 

of GDP data must be combined with the 60 BCEI GDP growth rate forecasts that are needed to 

estimate neutral GDP in Q1-1997. For example, the first estimate of neutral GDP Q1-1997 uses 

the GDP series that became available in April 1997 when the first estimate of Q1-1997’s GDP 

was released. The percentage differences between the first, second, and third estimates of GDP 

17 Initially, we hypothesized the comprehensive updates by the BEA over time may have biased the neutral GDP 

estimates downward, thus overstating how large the expectations gap was in 2001. (This would arise if actual GDP 

estimates were revised up from the addition of new, higher-growth spending categories while the real-time BCEI 

growth rate projections would be unchanged and would tend to underestimate growth.) However, as our real-time 

analysis in the next section shows, the expectations gap estimates derived from the latest GDP estimates are smaller 

than the real-time gap estimates. 
14 

are then taken against the first, second, and third estimates of neutral GDP that would have been 

available at each point in time. 

Figure 5: Real-time Estimates of the Expectations Gap 

Panel A of Figure 5 shows the first-, second-, and third-estimates of the expectations gap that 

would have appeared when a given quarter’s GDP numbers became available. For example, the 

Q1-1997 first, second, and third estimates (released in April, May, and June of 1997) indicated 

an expectations gap of about -2.0 percent. Since then, comprehensive updates made to the GDP 

estimates as of December 2020 indicate that the actual expectations gap (titled “Current” in 

Figure 5) for Q1-1997 GDP was only about -0.5 percent. Differences between the first, second, 

and third estimates of the expectations gap are very small, while comprehensive updates do lead 

to non-trivial revisions, especially in the late 1980s,1990s, and 2014-2018 when the latest 

expectations gap estimates tend to have a smaller absolute value than the real-time estimates. 

Exceptions occurred in 1997-99 and 2008-11 when the latest estimate was revised up or down 

respectively from its real-time counterpart.18

Panel B of Figure 5 computes a real-time CBO output gap series that we generated by combining 

as reported GDP data with CBO’s estimates of potential GDP that were published near the 

charted quarters. For example, the real-time output gap around late 2008 to early 2009 is derived 

from BEA data from June 2009 and CBO’s August 2009 Update to the Budget and Economic 

Outlook. The methodology used in Figure 5 combines the CBO’s various outlook publications 

(generally two per year) with the vintage of BEA data that was used to make their forecasts. The 

results show that CBO’s revisions to the output gap are just as large or even larger at times than 

those seen in the expectations gap. 

18 The BEA’s comprehensive benchmark revision in 1999 to include spending categories on software, intellectual 

property products and research and development is likely behind many of the larger revisions in the late 1980s and 

1990s as these categories were growing faster than average over this period so that their inclusion would imply a 

more positive (less negative) expectations gap. 
15 

5.2 Estimating a Forward-Looking Expectations Gap 

Here we illustrate how a forward-looking expectations gap can be derived from incomplete 

survey data, using Q4-2021 as an example. As outlined in Section 2, calculating the expectations 

gap requires calculating the percentage difference between actual GDP and neutral GDP. The 

Q4-2021 neutral GDP estimate would be the average of forecasts for GDP in that quarter that 

were submitted between January 2016 and December 2021. If survey data is only available 

through September 2020 (Q3-2020), with 15 quarterly forecasts in hand and 5 to be submitted, 

there are two ways to derive a forward-looking neutral GDP estimate. 

One approach is to average all forecasts that have been published to date:   

 Yഥ௧ା௝|௧,௩ ൌ 1

20 െ j ෍ Y௧ା௝|௧ି௛,௩ ሺ5ሻ

ଵଽି௝

௛ୀ଴

which gives them all equal weight. An alternative approach gives the most recently published 

forecast additional weight by assuming that it will be repeated for the (5) forecasts that have yet 

to be submitted for the quarter of interest. Averaging the forecasts available-to-date and the lastpublished forecast repeated for the j missing quarters in between the present and the future 

quarter of interest yields: 

Yഥ௧ା௝|௧,௩ ൌ 1

20 ෍ ൫Y௧ା௝|௧ି௛,௩ ൅ jY௧ା௝|௧,௩൯ ሺ6ሻ

ଵଽି௝

௛ୀ଴

This latter approach places greater weight on the most recent forecast. It assumes that the future 

forecasts of GDP are more likely to be represented by what is the latest forecast than by the 

forecasts made in any period prior. We focus on this latter approach. 

Taking the percentage differences between the latest forecast path of nominal GDP and a series 

of forward-looking neutral GDP estimates derived according to equation (5) or (6) yields a 

forward-looking nominal expectations gap series. The consensus forward-looking expectations 

gap series converges to zero at the five-year ahead horizon regardless of whether equation (5) or 

(6) is used.19 Since this drives the movements in all forward-looking expectations gap estimates 

at longer-horizons, we focus on the forward-looking estimates up to two-years ahead. 

Figure 6 illustrates how the BCEI-derived forward-looking expectations gap estimates would 

have appeared with the BCEI surveys released for July 1990 (panel A), March 2001 (panel B), 

19 For example, supposing the September 2020 BCEI survey was the latest, the consensus forecasts that Q3-2025 

GDP will be $26,410.5 billion and is the only forecast available for Q3-2025. Assuming, as equation (6) does, that 

the forecast of $26,410.5 billion will be repeated 19 times for the surveys that will be published between October 

2020 to September 2025, the calculation becomes 20*($26,410.5 billion)/20, an expectations gap of 0.0 percent. 

There is some underlying economic intuition for this assumption, in that sticky debt obligations that enable potential 

inefficiencies ex ante are expected to be resolved after 5 years. 
16 

December 2007 (panel C), and February 2020 (panel D) – the months a business cycle peak was 

eventually declared by the NBER for the last four recessions. 

 

Figure 6: Forecasts of the Nominal Expectations Gap 

The forward-looking expectations gap ranges are the percentage difference between the top 10 

(bottom 10) nominal GDP projections and a forward-looking neutral nominal GDP estimate that 

is derived only from the consensus nominal GDP forecasts using equation (6). Thus, Figure 6, 

shows the most optimistic and most pessimistic forward-looking paths for the expectations gap. 

It ignores forecaster disagreement about neutral GDP estimates that was discussed in Section 3.3 

and focuses only on the disagreement around the latest survey’s forecast path for GDP. 

We can evaluate the performance of the forward-looking expectations gap by computing the root 

mean square prediction errors (RMSE) across horizons. We compare the performance of the 

forward looking measures against a simple random walk that extends the last observed estimate 

of the expectations gap forward into the future and is a simplified version of the autoregressive 

prediction proposed in Beckworth (2020). 

Table 1 shows that the real-time BCEI-based forward-looking measures consistently have 

smaller RMSEs than the random walk out through four-quarters ahead. This is unsurprising as 
17 

the forward-looking measures at short-horizons contain most of the same information as the 

actual estimates of the expectations gap do. The forecasts are particularly useful through 2-

quarters ahead with RMSEs less than 1% but become less so at longer horizons where the 4-

quarter-ahead forecasts have RMSEs of almost 1.5%. The performance at shorter horizons is less 

accurate when using the latest estimates of the expectations gap, which include large revisions in 

the underlying data. 

Table 1. Expectations Gap Forecast Performance (1985 Q1 – 2020 Q2)

Horizon 

1st 2nd 3rd Latest 

BCEI RW BCEI RW BCEI RW BCEI RW 

0 0.18 0.65 0.13 0.70 0.15 0.73 0.97 1.25 

1 0.43 1.08 0.48 1.12 0.52 1.14 1.06 1.56 

2 0.72 1.49 0.76 1.53 0.79 1.54 1.20 1.89 

3 1.04 1.85 1.08 1.89 1.09 1.90 1.39 2.21 

4 1.37 2.20 1.39 2.23 1.40 2.23 1.62 2.50 

Focusing on periods that were later defined to be recessions by the NBER (Table 2), the forwardlooking measure continues to outperform the random walk, though performance deteriorates 

substantially at longer horizons. Now, only nowcast and one-quarter-ahead forecasts have 

RMSEs that are less than 1%, while the 4-quarter-ahead forecasts have RMSEs of about 3.7%. 

Table 2. Expectations Gap Forecast Performance During NBER Recessions 

Horizon 

1st 2nd 3rd Latest 

BCEI RW BCEI RW BCEI RW BCEI RW 

0 0.19 2.79 0.14 2.74 0.15 2.70 1.09 3.17 

1 0.57 3.65 0.63 3.59 0.62 3.56 1.29 4.06 

2 2.63 4.06 2.58 4.01 2.55 3.98 2.89 4.51 

3 3.45 4.26 3.39 4.22 3.35 4.19 3.73 4.72 

4 3.78 4.42 3.73 4.37 3.69 4.33 4.11 4.90 

5.3 Forward-Looking Estimates Near Business Cycle Turning Points 

Figures 7 and 8 show the evolution of the real-time estimates of the forward-looking 

expectations gap over four quarters near business cycle turning points. Within a particular 

quarter, the bars represent a consecutive monthly sequence of forward-looking expectations gap 

estimates for that quarter. The darkest-colored bar is the forward-looking expectations gap as it 

would have appeared in the month that NBER eventually declared to be the peak of the 

expansion. The bars that precede it are the forward-looking estimates from the BCEI surveys 

released the prior eight months. The three bars that follow it are from the three subsequent 

months’ BCEI surveys. The right-most bar for each quarter represents the expectations gap as it 

is currently estimated. 
18 

For example, in Panel A of Figure 7, shows the quarters around the 1990-91 recession. July 1990 

was the month that was ultimately declared to be the peak by the NBER. The sequence of 

forward-looking expectations gap for Q3-1990 is slightly negative for the BCEI surveys released 

between November 1989 and March 1990. After becoming positive between the April 1990 and 

July 1990 surveys, it turns sharply negative for the surveys submitted between August 1990 and 

October 1990. 

Figure 7: Real-time Forward-Looking Expectations Gap Estimates Near Expansion Peaks 

Before the 1990-91, 2001, and 2008-09 recessions the forward-looking gap estimates attain a 

high two- to five-months ahead of an expansions’ peak month. This does not apply to the 2020 

recession, which underscores the sudden and unexpected nature of the COVID-19 pandemic. The 

2001 recession is peculiar in that the expectations gap remains positive. Thus, a negative 

expectations gap is not a sufficient condition to signify a recession. Instead, a recession may be 

portended by consistent downward revisions to a given quarter’s forward-looking expectations 

gap. This is discussed further in Section 6. 

Figure 8 shows the evolution of the real-time estimates of the forward-looking expectations gap 

near the trough of recessions. In these panels, the darkest bars represent the expectations gap 

estimates from the BCEI-survey released during the month the NBER eventually declared to be 

the trough of a recession (March 1991, November 2001, June 2009; for the 2020 recession, we 

assume that the NBER will declare a trough to have occurred in June 2020). 
19 

A pattern of forward-looking expectations gap estimates near a recession trough is even less 

discernable relative to those near an expansion peak. In Panel A, around the 1990-91 recession, 

the forward-looking expectations gap for the four quarters charted cease being revised down by 

March 1991, which corresponds to the recession trough. 

Figure 8: Real-time Forward-Looking Expectations Gap Estimates Near Troughs 

In Panel B, the September 11, 2001 terrorist attacks have a sharp impact on forward-looking gap 

estimates. While the September 2001 survey (usually released the tenth day of the given month, 

i.e., in this case just before the attacks took place) signifies a positive forward-looking 

expectations gap, the subsequent measure based on the October survey drops sharply into 

negative territory. However, downward revisions to the forward-looking expectations gap ceased 

with the November survey, which is the month the NBER eventually declared to be a trough. 

In Panel C for the 2008-09 recession, the forward-looking expectations gap improved somewhat 

following the March 2009 survey until the June survey, which coincides with the month the 

NBER declared to be the trough. 

For the 2020 recession (Panel D), the forward-looking expectations gap estimates reached the 

lowest point in June. Since the forward-looking gap did not improve before and up to the June 
20 

2020 survey, it failed to serve as a leading indicator of the trough, assuming the NBER 

eventually declares June as the trough. 

Thus, for the 1990-91, 2001, and 2020 recessions, the real-time forward-looking expectations 

gap did not predict an eventual trough. However, in the 2008-09 recession, a slight improvement 

in the forward-looking expectations gap did appear two months before the eventual trough. 

Overall, persistent month-to-month differences between real-time forward-looking expectations 

gap estimates seems to predict business cycle turning points, but not always. This is because 

recessions and recoveries can often occur abruptly for reasons that were unforeseen by 

professional forecasters at the time. Therefore, a negative forward-looking expectations gap may 

be sufficient but is not necessary to signify a recession as illustrated by the 2001 recession. 

6 Conclusion 

In this paper we assess the expectations gap proposed in Beckworth (2020). The latter measure 

of economic slack is derived from professional forecasters’ historical expectations about the 

trajectory of the economy in contrast to conventional output gap estimates that are derived from 

model-specific measures of potential GDP. To overcome some of the limitations of Beckworth 

(2020) we develop and evaluate several alternative measures of the expectations gap using 

alternative surveys, different forecast horizons, individual forecasters’ estimates, nominal and 

real measures, and real-time and forward-looking estimates. 

Although the expectations gap is not particularly sensitive to the choice of forecaster survey it is 

sensitive to the range of forecast horizons over which neutral GDP is calculated. Moreover, there 

is considerable disagreement between individual forecasters about the future trajectory of the 

economy. Despite this, we identify four distinct episodes in which the entire range of individual 

forecasters’ expectations gaps provides an unambiguous signal about the state of the economy. 

There are meaningful and informative differences between the nominal and real expectations 

gaps, which contrasts with conventional output gap measures. This is exemplified by the nominal 

expectations gap detecting overheating during the run-up in housing prices during the mid-2000s 

that preceded the 2008-09 recession. Also, in 2015-16 the nominal expectations gap detects the 

so-called invisible recession while the real expectations gap and conventional output gap 

measures do not. Furthermore, although there have been considerable historical revisions to the 

expectations gap due to comprehensive updates in GDP, these revisions are in line with those 

experienced by conventional measures. 

Forward-looking measures of the expectations gap is generally informative up to two-quarters 

ahead and are always more informative than assuming there will be no change. Although 

forward-looking measures of the expectations gap do not always signal an oncoming expansion 

peak or recession trough, the direction of updates to the forward-looking expectations gap 

occasionally precedes a business cycle turning point. 

Overall, the expectations gap is a useful measure of economic slack. Consequently, it may be 

useful to shift the emphasis from “navigating by the stars” to navigating by what agents expect. 
21 

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