Corresponding author: Francesco Grigoli ( fgrigoli@imf.org ) © 2015 Nonprofit partnership “Voprosy Ekonomiki”.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BYNCND 4.0), which permits to copy and distribute the article for noncommercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Grigoli F, Herman A, Swiston A, Di Bella G (2015) Output gap uncertainty and realtime monetary policy. Russian Journal of Economics 1(4): 329358. https://doi.org/10.1016/j.ruje.2016.02.001

Output gap estimates are subject to a wide range of uncertainty owing principally to the difficulty in distinguishing between cycle and trend in real time. We show that country desks tend to overestimate economic slack, especially during recessions, and that uncertainty in initial output gap estimates persists several years. Only a small share of output gap revisions is predictable based on output dynamics, data quality, and policy frameworks. We also show that for a group of Latin American inflation targeters the prescriptions from monetary policy rules are subject to large changes due to revised output gap estimates. These explain a sizable proportion of the deviation of inflation from target, suggesting this information is not accounted for in realtime policy decisions.
output gap, monetary policy, policy rule, data revisions, realtime, uncertainty, Brazil, Chile, Colombia, Mexico, Peru, inflation target, business cycle
“What is it that no one can see, hear, smell, taste or touch, yet everyone knows is there? Answer: the output gap.” — Caroline Baum, Bloomberg, April 12, 2010
Output gap measures are used as if they were essential and reliable for assessing macroeconomic policies. Both fiscal and monetary policy reaction functions use output gap estimates as an input in assessing the appropriate settings for relevant instruments (e.g., the structural fiscal balance or the interest rate). While fiscal and monetary authorities analyze a wide variety of indicators in assessing the cyclical position of the economy (including deviations of unemployment from its natural rate), they frequently resort to the output gap to summarize their assessment of economywide spare capacity.
Despite being widely used to formulate policy recommendations, initial output gap estimates are characterized by large uncertainty. This has been extensively documented in the literature. For instance,
This paper revisits the issue of output gap uncertainty by analyzing properties and determinants of realtime output gap estimates from different sources for the period 1990–2014. It focuses on the changes in output gap estimates that arise due to expost GDP data revisions and changes in the decomposition of actual GDP data into its cyclical and trend components. It empirically assesses whether realtime data can predict how much the output gap will be revised later. The paper then analyzes the implications of output gap uncertainty for five Latin American economies that have implemented inflation targeting over the last decade. Our results suggest that realtime estimates of output gap are highly unreliable. In particular, country desks tend to overstate economic slack. In addition, we show that revisions are substantial (especially during recessions), persistent, and, to a large extent, unpredictable. Finally, we find that revisions help to explain deviations of inflation from the target, suggesting that this information is not accounted for in realtime policy decisions.
The paper is organized as follows. Section
This section examines the statistical properties of output gap estimates and their revisions, in order to evaluate the degree of confidence that can be attached to initial assessments of an economy's cyclical position.
The output gap is an unobserved, estimated concept, and therefore not known with certainty. It is defined as the deviation of actual from potential output, as a percent of potential. In equation (1) below, y denotes actual output (measured by real GDP) and y* represents potential output, which is defined as the output an economy could produce if all factors of production were operating at their full employment rates of capacity. The output gap is denoted by $\overline{y}$:(1)
A negative (positive) sign for the output gap indicates that output is below (above) potential. Estimates of potential output are heavily influenced by the average level of an economy's production over time. Revisions to the initial estimate of the output gap could occur as subsequent developments change estimates of the economy's productive capacity in previous periods.
Sources of revisions to output gap estimates.
As shown in
This paper focuses on the third and fourth sources —revisions to the output gap arising from data revisions and those arising from changing the decomposition of actual data into its cyclical and trend components. These sources are present in forecasts and in all expost estimates until the final estimate, as data are revised and estimates of potential output take into account both data revisions for period t and developments in subsequent periods.
This study will be restricted entirely to expost estimates — those made after data for the period under study has been released — in order to isolate the impact of data revisions and potential output uncertainty and ensure that deviations related to policy reactions and forecast error do not affect the findings. Modeling the realtime impact of policy reactions and deviations arising from forecast errors are outside the scope of the analysis.
This paper uses data and forecasts from the International Monetary Fund's (IMF) World Economic Outlook (WEO), released twice a year (in the spring and the fall). The WEO database consists of macroeconomic data and forecasts submitted by country teams and vetted by the IMF's Research Department for both internal and multilateral consistency. There is no prescribed estimation methodology, but the estimates are used by the IMF in discussions with country authorities over appropriate economic policies, underscoring the importance of an accurate assessment.
Given the importance of working only with expost estimates, the vintage from which to draw the data is critical. The spring WEO was released in May up through 2001 and in April thereafter; the fall version is typically released in October, and occasionally in September. Given the production lags, forecasts for the spring publication are performed during February or March. Given this timeline, in the spring WEO real GDP data for the previous year will continue to be an estimate or forecast for some countries. For this reason, the analysis is performed with the fall vintages.
Data are available since 1991. Given that the final estimate of the output gap is that measured seven years after the period in question, the available WEO vintages allow the calculation of initial estimates and subsequent revisions up to the final estimate from 1990 to 2007. The WEO database contains realtime estimates of the output gap made by country desks for many advanced economies throughout this period. Estimates for many other economies, however, begin only in 2008.
In order to cover as many countries as possible, we estimate output gaps using potential GDP obtained by applying the HodrickPrescott (HP) filter on real GDP data from the WEO and compare with the estimates from country desks where available. As shown in
HP filter smoothing parameter.
Output gap: Initial estimates and revisions (percent of potential GDP).
Initial assessments of an economy's cyclical position are subject to a high degree of uncertainty.
Uncertainty over the output gap persists for several years after the period under analysis.
Marginal output gap revisions by vintage (absolute value; percent of potential GDP).
Source: Authors’ calculations.
In addition, initial assessments of the cyclical position overestimate the amount of slack in the economy. Actual output is 1.0 percent below potential output in initial estimates, but only 0.2 percent below potential in final estimates, and median revisions to the output gap exceed 0.5 percent of potential for all types of countries (
Two factors interact to produce excess capacity bias. First, initial estimates of economic activity tended to be revised upward in later vintages (
Initial assessments of an economy's cyclical position are least reliable during recessions.
The key features of initial assessments of an economy's cyclical position and its subsequent revisions are 1) a high degree of uncertainty that persists several years beyond the period under analysis; 2) initial estimates have an excess capacity bias, overestimating the amount of spare capacity in an economy; and 3) increased uncertainty around cyclical turning points, in particular during economic downturns.
In order to ensure that these features of the data are not unique to the WEO dataset or our use of the HP filter to estimate potential output and the output gap, we perform several robustness checks.
First, we use HPfiltered data generated by setting λ equal to 6.25.
Second, we use estimates of the output gap from two crosscountry sources: the OECD's Economic Outlook database and the WEO database. The December edition of the Economic Outlook was used, as its release coincides most closely with the Fall WEO. As with the WEO estimates, the OECD estimates are submitted directly by country teams using their own judgment as to the amount of spare capacity in each economy. Using estimates that rely on the judgment of analysts covering the economies in question should reveal whether the use of the HP filter is driving the results.
Both the WEO and OECD data cover mostly advanced economies, so
Third, for the United States we analyze output gap estimates based on a production function approach published by the Congressional Budget Office (CBO; see
Fourth, the results are also robust to adjusting assumptions regarding the filter and sample.
Overall, these results underscore the challenges facing policymakers when setting policy based on assessments of an economy's cyclical position. Assessments made at the time of policy decisions are likely to be revised substantially in subsequent periods, and they likely overstate the degree of excess capacity in the economy. In addition, there is evidence these problems are more acute during turning points, as revisions tend to be larger during recessions.
The previous section establishes the wide range of uncertainty that surrounds initial estimates of the output gap. This section looks at whether output gap revisions can be predicted based on either countryspecific characteristics or the country's position in the business cycle at the time of the initial estimate. Although we find several significant determinants of output gap revisions, a large share of revisions remains unexplained, suggesting that they may not be predictable at the time policy decisions are made.
Some variables may explain the direction of subsequent output gap revisions, while others may only be informative about the magnitude of revisions. In order to maximize the explanatory power of the information at our disposal at the time of initial estimates, we attempt to explain the size of output gap revisions rather than the direction in which the revisions occur.
where α is the intercept, Xi,t  t+1 is a matrix of variables including the set of covariates for country i at time t and measured at time t+1, Di is a matrix including other timeinvariant covariates measured at the most recent point in time, β and δ are the coefficients on these matrices, and ɛit is a mean zero error term that captures unexplained heterogeneity.
Equation (2) is estimated using ordinary least squares (OLS) applied to a pooled panel sample of annual observations, correcting the standard errors for heteroskedasticity and autocorrelation. As a robustness check, we also estimate equation (2) with a further correction of the standard errors for crosssectional dependence.
The selection of the control variables Xi,t  t+1 and Di included in the specifications relies on our understanding, guided by previous empirical research (see, in particular,
In order to maximize the usefulness of the findings to policy decisions, we also investigate the determinants of output gap revisions that are large enough to change the sign of the gap, since realtime assessments of whether the economy is above or below potential output play a key role in policy decisions. To investigate the determinants of changes in the sign of the output gap, we estimate the following populationaveraged panel probit model
where Φ is the probability function, and ${S}_{i,t(t+1,t+7)}^{\overline{y}}$ is a binary variable taking the value one when the sign of the output gap of country i at time t measured at time t+7 is the opposite of the sign of the same output gap measured at time t+1. To avoid mild fluctuations around potential GDP, we consider only episodes in which the output gap revision is larger than half a percentage point of potential GDP. Our estimations perform a correction for heteroskedasticity and autocorrelation of the standard errors.
We group the baseline regressors into four categories. A first category includes variables related to domestic or world GDP dynamics. In particular, we control for the size of the output gap measured at t+1. A very large positive or negative output gap may signal a change in trend growth that is incorporated only gradually into estimates of potential output and thus we expect a positive impact on the size of revisions. Also, we include domestic and world real GDP growth surprises in time t (measured at time t+1 to have actual figures), which are defined as the deviation of domestic (or world) real GDP growth from its mean within the last 10 years. Thus, when a surprise in growth occurs, either domestic or worldwide, it increases the difficulty of decomposing actual output data into its trend and cyclical components, negatively affecting the ability to estimate the output gap and increasing the expected size of its revisions.
A second category of variables attempts to gauge macroeconomic uncertainty. To this end, we use the standard deviation of domestic real GDP growth over the last 10 years measured at t+1, as a proxy of historical volatility in the economy. We also include the share of natural resource rents (or economic profits) in GDP to proxy natural resource price movements that are not necessarily reflected in inflation, as well as volume changes. In a broader sense, this variable is a proxy for structural changes in the economy.
A third category of variables captures the presence of policy frameworks. In particular, we include dummy variables for the presence of inflation targeting and fiscal rules that are specified in terms of some fiscal aggregate adjusted for the cycle (here we call them cyclical fiscal rules). These frameworks should activate countercyclical policies which should help keep output relatively near its trend level, reducing the size of revisions. Fiscal rules, however, are often introduced when fiscal discipline is weak and their adoption can be accompanied by significant adjustments when conditions for triggering escape clauses are not met. Thus, the expected effect on output gap revisions is ambiguous.
A last category of variables is supposed to capture the degree of statistical capacity common to different groups of countries. Advanced economies are likely to have good and timely data and thus revisions to actual data and the output gap are expected to be smaller. In contrast, data timeliness and availability is more heterogeneous in lowincome countries (LICs), possibly affecting the reliability of initial releases of GDP data and increasing the size of output gap revisions. This is similar to what happens in a number of small economies (those with a population below the 10^{th} percentile of the population distribution). Beyond data quality, LICs and small economies may be subject to shocks (such as natural disasters) whose effects are hard to decompose between the trend and the cycle in real time. These three factors are represented by dummy variables.
Determinants of the absolute revisions of the output gap, baseline (dependent variable: absolute revisions of the output gap at t+ 7 compared to t+ 1).
Column 1 presents the results for our preferred estimation. Most of the variables’ coefficients are significant and take the expected sign. A one percentage point increase in the (absolute value of the) output gap is associated with a 0.11 percentage point increase in its revision. Similarly, real GDP growth surprises positively affect revisions. A one percentage point increase in the deviation of domestic real GDP growth from its past 10 years’ mean raises the revision by 0.07 percentage points, while a one percentage point increase in the deviation of the world real GDP growth from its past 10 years’ mean raises the revision by 0.19 percentage points.
Evidence on the effects of macroeconomic uncertainty on output gap revisions is mixed. The coefficient on historical growth volatility is insignificant, suggesting that historically volatile countries are not subject to greater uncertainty around output gap estimates. However, estimating output gaps for resource rich countries is more challenging, as an increase of one percentage point in rents as a share of GDP brings about an increase in the revision by 0.02 percentage points.
Countries with inflation targeting regimes have lower output gap revisions. Indeed, these countries have output gap revisions that are 0.36 percentage points lower than other countries, holding other factors constant. In contrast, fiscal rules are not reflected in any significant change in the size of the revisions.
Quality (including timeliness) of data is a statistically significant determinant of the revisions. OECD countries have revisions over half a point smaller than those of other countries, while LICs have revisions that are about half a point larger than other countries. Small countries have even larger revisions —0.79 percentage points greater than the rest of the sample.
Some robustness checks are performed on the preferred specification. The results after correcting for crosssectional dependence of standard errors are very similar, suggesting that such dependence is not pervasive in the data (Column 2). Also, the results when using WEO output gap estimates (Columns 3 and 4) and OECD estimates (Columns 5 and 6) are generally consistent, though the size of the statistically significant coefficients is larger in some cases. For example, the magnitude of the coefficients for the size of the output gap and surprises in world real GDP growth are about six and four times larger, respectively, when WEO data are used, and 1¼ and 4 times larger when OECD data are used. On the other hand, the coefficients for real GDP growth volatility and inflation targeting regime are significant when OECD data are used and maintain a similar magnitude.
Two variables become statistically significant when using OECD output gap estimates. Rents (as a share of GDP) take a negative sign suggesting that the size of the revisions is smaller when the country is resource rich. This (rather counterintuitive) result is mainly driven by the large rents in Norway, Canada, and Australia, countries with a high degree of macroeconomic stability. The estimation of a regression including an interaction term between rents and a dummy variable taking a value of one for these three countries yields a nonsignificant coefficient on rents. All these robustness checks should be taken with caution as the sample is only a fraction of the one in Column 1. Also, the subset of countries used in Columns 3 to 6 may suffer from selection bias because the countries included are mainly advanced economies.
Finally, we run the same specifications in Columns 1 and 2 using HPfiltered WEO data that are generated by λ equal to 6.25. The results are very similar to the ones reported in Columns 1 and 2 and suggest that the choice of the smoothing parameter does not affect the main conclusions.
The goodness of fit of the different specifications falls in the 10 to 41 percent range. This suggests that a large component of the revisions behaves as a white noise process, and thus, it cannot be explained by factors known to policymakers.
We also estimate the baseline specification without dummy variables for country groups. One may argue these dummies pick up effects other than the ones they are constructed for and that, as a result, the explanatory power may be even lower than in the baseline estimation. The results, however, suggest that this is not the case as the continuous variables present similar coefficients and the Rsquared is close to the one of the baseline, so the results are not shown.
In order to reduce the likelihood of omitted variable bias, in
Determinants of the absolute revisions of the output gap, extensions (dependent variable: absolute revisions of the output gap at t +7 compared to t + 1; HP filtered data).
Social or political conflicts can be detrimental to output gap estimation because of the destruction of human and physical capital (including assessing the impact on the economy's productive capacity). To capture this, we include a dummy variable taking a value of one if the loss of life due to conflict is considerable, and expect it to be positively associated with the size of the revisions. We find the coefficient on this dummy to be statistically insignificant (Column 2).
Also, we control for institutional quality. Corruption or low bureaucratic quality may negatively affect data quality and data production processes. For example, if institutional quality is weak, there may be scope for data manipulation with the aim of obtaining political advantage. Hence, we expect the coefficients on both variables to be negative. The estimation yields statistically insignificant coefficients (Columns 3 and 4).
Finally, we introduce information about future GDP growth. In principle, a sharp acceleration or slowdown in growth after year t should play a role in revisions to the estimated output gap in t by changing the decomposition of actual data into trend and cycle. We measure the change in future growth by taking the absolute value of the difference in average GDP growth between the five years following t and the five years preceding it. The coefficient on this variable is significant and indicates that an absolute change of one percentage point in future growth increases the size of the output gap revision by 0.12 percentage points. The incorporation of this variable turns the coefficient on the (absolute) size of the output gap insignificant, suggesting some redundancy between the two. Moreover, the increase in explanatory power is modest (Column 5).
Similar to the robustness check for the baseline specification, we exclude the dummy variables for country groups and reestimate Columns 1 to 5. While the coefficients for the continuous variables show similar magnitudes, bureaucratic quality, and control of corruption turn significant with the expected negative sign, suggesting a fairly high degree of correlation with the excluded dummies (results not shown).
Determinants of the probability of the output gap changing sign (dependent variable: binary variable, 1 if output gap changes sign at t + 7; HP filtered data; marginal effects).
Macroeconomic uncertainty affects the probability of a sign change in the output gap during the revision period, but the size of the coefficient is relatively small. An increase in inflation by one percentage point increases such probability by 0.002 percent. Being an inflation targeter reduces the probability of a sign change by 0.09 percent. Interestingly, countries with cyclical fiscal rules are more likely to observe changes in the sign of the output gap during the revision period by 0.12 percent.
Statistical capacity matters. Consistent with the results of Tables
Columns 2 to 6 report the results of extensions to the baseline specification. The baseline regressors are robust, with the exception of real GDP growth surprises which loses significance when the institutional quality variables are included and the dummy for small economies is dropped. Among the additional regressors, SDDS (but not GDDS) is significant and takes the expected negative sign. A shift in future GDP growth relative to past GDP growth reduces the probability of the output gap switching sign. These results are robust to the exclusion of country group dummies from the specification.
Overall, these regressions predict a low share of the variation in output gap revisions. Given that they make use of some information that is not known until after the period under analysis, it is reasonable to expect that the predictability of output gap revisions ex ante, when it would be useful for policy decisions, is even lower.
This paper has illustrated the wide range of uncertainty that typically characterizes assessments of the cyclical position of economies around the world. It has also shown that only a small share of this uncertainty is likely to be explained by factors known to policymakers in real time. This section illustrates some policy implications of these findings, focusing on five Latin American economies (LA5) that have implemented active countercyclical monetary policies over the last decade.
The historical output gap data and revisions described above can be used to construct a confidence interval around any initial or revised estimate of the output gap. The width of the confidence interval will vary by country, depending on the historical distribution of its output gap revisions. It will also vary by the vintage of revision, with a wider confidence interval for an initial estimate than for a revised estimate that is closer in time to the final estimate.
The confidence intervals provide a broad view of how uncertain the cyclical position of any economy is, but are based on annual observations, so are less applicable for monetary policy decisions that make use of higherfrequency data. In this section, we construct realtime quarterly output gap estimates and use them to estimate monetary policy reaction functions based on realtime data.
The LA5 mentioned above all adopted inflation targeting as their monetary policy framework between 1999 and 2002 (
The output gap is not the only indicator of the degree of spare capacity, but it is one of the broadest and is frequently used both in models of monetary policy and in policy decisions. Alternative indicators such as the unemployment rate can also be useful but have other shortcomings, including being dependent on labor participation rates, which can change over time. Combinations of variables may outperform any individual variable, but for each indicator of spare capacity the fundamental challenge is the same as for the output gap —decomposing observed data into its cyclical and trend components. Thus, while central banks analyze a wide variety of indicators in assessing the cyclical position of an economy, this section uses the output gap to summarize economywide spare capacity and illustrates the implications of output gap revisions for the appropriate settings of monetary policy.
An inflationtargeting central bank sets policy so as to minimize the deviation of actual inflation πt from the target π^{*}, as in the following loss (γ) function:(4)
Since monetary policy affects economic activity with a lag and activity affects prices with a lag, the policy instrument rt — which in the countries analyzed here is the rate at which the central bank makes shortterm loans to commercial banks — is set with respect to the expected value at time t of the deviation of future inflation (at time t+i) from the target (πt+i  t – π^{*}) given the current information set:(5)
Conceptually, any information that could help predict future inflation would have a place in the central bank's reaction function. This could potentially include a wide array of variables or nonquantitative information (for example, on prospective harvests of key agricultural products). In practice, the inflation expectations of market participants should account for all publiclyavailable information relevant to inflation at a given point in time, and could thus serve as a proxy. Given this paper's interest in the impact of domestic capacity utilization on inflation, the output gap (${\overline{y}}_{t}$) is included separately.
where ${\pi}_{t+1t}^{m}$ represents the inflation expectations of market participants at the horizon relevant for monetary policy. Expectational channels are typically strong in inflationtargeting regimes because of the forwardlooking nature of policy, which advocates the use of the lagged policy rate in empirical estimation (Woodford,
The persistence parameter on the lagged monetary policy rate is ρ, απ and ${\alpha}_{\overline{y}}$ are the responses of monetary policy to expected inflation and the output gap, respectively, and ηt is the error term. The expected future output gap is not included because the transmission lag from the output gap to inflation implies that the current or lagged output gap is more relevant for future inflation; here the first lag is used since the initial estimate based on actual data is available by the end of the following quarter. However, given the method for estimating the output gap described below, even the lagged output gap embodies information on the expectations of market participants concerning output growth in subsequent quarters (the results are insensitive to using the forecast of the contemporaneous output gap).
The output gap is computed using an HP filter with a smoothing parameter of 1600. In order to mitigate the endpoint problem implicit in filtering, actual data on real GDP was merged with the real GDP growth expectations data to form a series extending between five and eight quarters beyond the endpoint of actual data at any given point in time. This extended series is then filtered, and the output gap calculated for the last available data point.
Quarterly output gap estimates for LA5 economies (output gap in percent of potential GDP).
Sources: National authorities; and authors’calculations.
Nevertheless, there are some substantial differences. For Brazil, the latest estimate suggests that the economy was operating at a higher rate of capacity utilization from 2010 to 2012 than given by initial estimates. Initial estimates of output relative to potential were also revised up substantially in Chile and Mexico from 2006 to 2008 and Colombia in 2008. Initial estimates for Peru signaled that output was above potential from 2005 to 2007, a finding that was later reversed as potential output was subsequently revised upward.
In order to evaluate the robustness of such realtime output gap estimates as inputs to monetary policy decisions, we estimate policy reaction functions as in (7). For each country these reaction functions use the realtime output gap estimated above and the inflation expectations of market participants surveyed by the respective central banks. The monetary policy rate is measured as the endquarter rate; thus, it should take into account all information available as of the last month of the quarter, including real GDP data for the previous quarter, plus expected inflation and real GDP growth in the central bank survey from that month. Estimation begins in 2005, coinciding with the availability of realtime GDP data for the LA5 countries.
Monetary policy reaction functions (dependent variable: monetary policy rate).
The monetary policy reaction functions estimated above can be combined with revised real GDP data to calculate the extent to which the policy prescriptions formulated in real time would deviate from the ideal policy calculated ex post using revised data. The ideal policy calculated ex post could not have been implemented in real time since the data informing the policy were not available. The purpose of the calculation is to demonstrate the potential inaccuracy of policy prescriptions relying heavily on an estimated output gap that is susceptible to large revisions.
Policy deviations owing to output gap revisions (actual interest rate minus revised prescription from reaction function, in percentage points).
Source: Authors’ calculations.
Following directly from the magnitude of output gap revisions presented in earlier sections, the deviations in policy prescriptions generated by these revisions are substantial. Deviations of over 100 basis points occur in multiple episodes across all countries. Some episodes are shortlived, but there are several instances in which these deviations last for over a year, reflecting the tendency for output gap revisions to display a high degree of persistence.
Revisions to policy prescriptions based on economic conditions in the current quarter are substantial, but a central bank may react to broad trends in economic activity spanning multiple periods. Averaging across periods may help to reduce the noisetosignal ratio in the data. To evaluate whether this kind of policy rule would generate policy prescriptions that are less susceptible to revision, we estimate reaction functions using a threequarter moving average of the output gap and calculate the deviations in policy prescriptions owing to output gap revisions.
The previous section showed that prescriptions from policy rules relying on the output gap are subject to substantial revisions. However, under inflation targeting these revisions only pose a problem to the extent that they contain information about inflation that is not otherwise accounted for in the central bank's actual policy decisions.
If output gap revisions are not related to deviations of inflation from the target, this demonstrates that the central bank is able to use other information to assess output relative to potential and adjust accordingly to keep inflation on target. However, if output gap revisions are related to deviations of inflation from the target, this suggests that the information regarding inflation that these revisions contain is not found in other data that the central bank has access to at the time of its policy decisions.
To evaluate the informational content of output gap revisions for inflation, we run regressions for either headline or core inflation on either the initial estimates of the output gap or revisions to the gap (the final estimate minus the initial one), using equations of the following form:(8)
where ${\overline{y}}_{i}$ is either the initial estimate of the output gap or the revision. It has already been established that initial estimates of the output gap tend to be measured with error. In regressions using the initial estimates, this measurement error would bias the βi coefficients in equation (8) toward zero.
Given transmission lags from the output gap to inflation, we include four lags. We run a separate set of regressions without the contemporaneous term for the output gap to ensure that simultaneity between output and inflation (owing to supply shocks, for example) does not drive the results.
Given the persistence of estimated output gaps and their revisions, the question of interest is whether the output gap terms in equation (8) are jointly significant for inflation. We perform Wald Ftests (robust to heteroskedasticity and autocorrelation) to measure the joint significance of the output gap terms in the equation.
Output gap as a predictor of inflation (probabilities of Wald Ftests measuring the joint significance of the output gap terms on inflation).
These findings suggest that noisy initial estimates of the output gap surround with uncertainty the formulation of appropriate countercyclical monetary policy. They also show that information that is important for inflation is not taken account in real time, precisely because the level of economic activity relative to its potential is not known with much certainty until long after policy decisions have been made. This underscores a key weakness of monetary policy decisions relying on realtime assessments of the cyclical position of the economy, especially in emerging economies where there are greater challenges in distinguishing between trend and cycle.
This paper illustrates the wide range of uncertainty that is often associated with country desks’ and market participants’ output gap assessments, which highlights their low reliability for policy recommendations. We find that initial assessments of an economy's cyclical position overestimate the amount of slack in the economy, and that revisions are persistent, especially during recessions. The paper also shows that only a small share of this uncertainty can be explained by factors known to policymakers in real time, and that trying to explain whether the initial estimate of the output gap will change sign is equally challenging. In particular, we find that output gap revisions are positively associated with the absolute value of the initial estimate of the output gap, and similarly, that real GDP growth surprises tend to make subsequent output gap revisions larger. Evidence on the effects of macroeconomic uncertainty on output gap revisions is mixed, while quality of data is a statistically significant determinant of the revisions. In addition, countries with inflation targeting regimes have lower output gap revisions, possibly owing to a more countercyclical monetary policy which results in a smaller output gap.
Output gap data and revisions can be used to construct confidence intervals around any initial or revised output gap estimate. The width of the confidence interval will vary by country, and by the vintage of the revision, with a wider confidence interval for an initial estimate than for a revised estimate. We find that, at the time of the initial estimate confidence intervals are usually so large as to span positive and negative values for the output gap. In other words, only in rare cases is there a high degree of certainty about whether policy should be contractionary, neutral, or expansionary. These results underscore the challenges policymakers face when making policy decisions that require an assessment of the economy's cyclical position.
The paper analyzes the implications of output gap uncertainty in the case of five Latin American economies (Brazil, Chile, Colombia, Mexico and Peru) that adopted inflation targeting as their monetary policy framework between 1999 and 2002. Assessing the economy's actual level of output relative to its potential is then a key issue for these countries, as the degree of spare capacity is typically an important predictor of future inflation. We find that monetary policy reaction functions using revised (more accurate) real output gap estimates result, in many cases, in policy prescriptions that are substantially different than those prescribed using initial estimates of the output gap. In addition, revised estimates of the output gap are positively and significantly correlated with inflation, suggesting that this information is not accounted for in realtime policy decisions.
These findings suggest that information that is important for inflation is not taken into account by central banks in their policy decisions. This occurs, at least in part, because the level of economic activity with respect to its potential is not known with much certainty until long after policy decisions have been made. The difficulty in distinguishing between cyclical fluctuations and shifts in the trend rate of growth underscores a key weakness of monetary policy rules relying on realtime assessments of the cyclical position of the economy, even when policymakers consult a large set of indicators.
We would like to thank Tamim Bayoumi, Patrick Blagrave, Olivier Blanchard, Alexander Culiuc, Valerie Cerra, Ana Corbacho, Ernesto Crivelli, Przemek Gajdeczka, Roberto GarciaSaltos, Huidan Lin, Luca Ricci, Lars Svensson, Alejandro Werner, Aleksandra Zdzienicka, and participants of the IMF Seminars held in September 2014 and March 2015 for helpful comments and suggestions.
Countries included in the analysis.
Description and sources of variables.
An annual frequency is assumed but the principles translate to any frequency.
As noted in Baxter and King (1995), setting λ to 10 or below closely replicates the statistical properties of the BaxterKing filter.
We run the HP filter over all available historical data plus the forecast available in the WEO database to mitigate endpoint problems.
See Appendix A for a complete list of countries in each group.
Clearly, turning points marking an acceleration of an economy could also be analyzed. Given that potential growth rates differ across economies, negative real GDP growth (especially at the annual frequency) may not catch all cyclical turning points, but it is probable that most observations in this subsample are turning points (with the most likely exception being economies in an extended period of negative growth). The results hold when negative growth is defined using the final estimate rather than the initial estimate.
See Ravn and Uhlig (2002)
The estimates are published in January of each year. Vintages are aligned with the WEO and OECD estimates published the preceding September/October and December, respectively, such that the first retrospective estimate of the output gap in year t is assumed to be made in January of year t+ 2; revisions are then made until the final estimate in year t + 8.
Results for subsamples are not shown since they are very similar to the baseline specification, but they are available from the authors upon request.
Note that the revisions of output gap estimates made ex ante would be even less predictable. See Appendix A for a detailed description of the variables.
The alternative is to use logit regressions, assuming an error term that is logistically distributed. As a robustness check, we perform logit regressions that are not shown because they return similar results. Results are available from the authors upon request.
The estimated coefficients of a probit model do not quantify the influence of the covariates on the probability of a sign change of the output gap because they are parameters of the latent model. As such, they only measure the effect of a regressor on the latent propensity for a positive result. The effect of a unit change of a covariate on the dependent variable when the other covariates are constant is represented by the marginal effect. This can then be interpreted similarly to the linear regression coefficient, which directly measures the marginal effect of an explanatory variable on the dependent one. Hence, for the probit estimations we only report the corresponding marginal effects.
Since there are no vintages available for rents in percent of GDP, we assume that the data are the same as at time t+ 8.
We also run a specification including a dummy taking value one during the 1990s to explore whether there was a change over time in the size of the revisions. However, the coefficient is not statistically significant.
Results are available from the authors upon request.
The difference between the two standards is the level of data requirements, with the SDDS being more demanding.
As in Ley and Misch (2013), we test if countries with an IMF program have higher output gap revisions (see Dreher et al., 2008), and the coefficient is insignificant.
The output gap is also an indicator for which forecasts tend to be more readily available. This permits estimation of the cyclical and trend components over both historical and forecast data points, thus mitigating the endpoint problem found in most filtering methods.
This will directly capture the central bank's response to the output gap, implicitly incorporating the central bank's expectation of the impact of the output gap on inflation.
GMM estimation gives similar results. For Brazil, a more backwardlooking specification with current inflation is used as it yields more stable coefficients.
In this rule, the central bank responds to an average of the output gap in the previous two quarters and market expectations of the output gap in the current quarter.
The findings are the same when using the final estimate of the output gap rather than just the revision.