The Output Gap: Method Choice, Data Revisions, and Predictive Performance

Kumar, Labesh (2026): The Output Gap: Method Choice, Data Revisions, and Predictive Performance.

Abstract

This paper compares eight widely used methods for estimating the output gap, ranging from simple deterministic trends to state space models, using both revised and real time U.S. quarterly data from 1980 onward. The resulting measures differ heavily across approaches. Average gap estimates vary by nearly four percentage points, volatility differs by an order of magnitude, and correlations across methods span from strongly positive to negative. Stability across data vintages also varies substantially. Hamilton type filters show relatively strong agreement between real time and final estimates, while simpler trend based methods are considerably less stable. These differences matter for empirical inference. The choice of output gap measure has important implications for Phillips curve estimates and for forecasting performance. Beveridge Nelson decompositions display strong predictive power for inflation when estimated using revised data but perform less well in real time, whereas refined Beveridge Nelson and modified Hamilton filters deliver more consistent results across vintages. Time varying analysis shows that the relationship between economic slack and inflation strengthens during periods of macroeconomic stress, including the early 1990s recession, the global financial crisis, and the post-pandemic period, rather than declining monotonically. For output growth forecasting, HP filter gaps reduce forecast errors using revised data, while unobserved components models perform best in real time. Although Beveridge Nelson based measures are informative for inflation, they tend to worsen growth forecasts. Combining forecasts across gap measures, particularly using Bates Granger weights, yields more reliable performance by offsetting weaknesses of individual methods. Overall, the findings highlight that methodological uncertainty in measuring slack translates directly into policy uncertainty, cautioning against exclusive reliance on any single output gap estimate.

• Item Type: MPRA Paper
• Language: English
• Keywords: Output gap, trend-cycle decomposition, real-time data, Phillips curve, forecast combination
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

Aiolfi, M., & Timmermann, A. (2006). Persistence in forecasting performance and conditional combination strategies. Journal of Econometrics, 135(1–2), 31–53.

Andriantomanga, Z., Kishor, N. K., & Kumar, L. (2026). Interest rate misalignments and monetary policy, Evidence from U.S. states. Economic Inquiry. https://doi.org/10.1111/ecin.70064

Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417–427.

Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Journal of the Operational Research Society, 20(4), 451–468.

Berge, T. J., & Jordà, Ò. (2011). Evaluating the classification of economic activity into recessions and expansions. American Economic Journal: Macroeconomics, 3(2), 246–277.

Beveridge, S., & Nelson, C. R. (1981). A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the 'business cycle'. Journal of Monetary Economics, 7(2), 151–174.

Canova, F. (1998). Detrending and business cycle facts. Journal of Monetary Economics, 41(3), 475–512.

Chauvet, M., & Potter, S. (2005). Forecasting recessions using the yield curve. Journal of Forecasting, 24(2), 77–103.

Clark, P. K. (1987). The cyclical component of US economic activity. Quarterly Journal of Economics, 102(4), 797–814.

Clark, T. E., & McCracken, M. W. (2009). Combining forecasts from nested models. Oxford Bulletin of Economics and Statistics, 71(3), 303–329.

Clark, T. E., & West, K. D. (2007). Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics, 138(1), 291–311.

Croushore, D. (2006). An evaluation of inflation forecasts from surveys using real-time data. Federal Reserve Bank of Philadelphia Working Paper.

Croushore, D. (2011). Frontiers of real-time data analysis. Journal of Economic Literature, 49(1), 72–100.

Croushore, D., & Stark, T. (2001). A real-time data set for macroeconomists. Journal of Econometrics, 105(1), 111–130.

Diebold, F. X., & Shin, M. (2019). Machine learning for regularized survey forecast combination, Partially-egalitarian Lasso and its derivatives. International Journal of Forecasting, 35(4), 1679–1691.

Edge, R. M., & Rudd, J. B. (2016). Real-time properties of the Federal Reserve's output gap. Review of Economics and Statistics, 98(4), 785–791.

Elliott, G., & Timmermann, A. (Eds.). (2013). Handbook of economic forecasting. Elsevier.

Estrella, A., & Mishkin, F. S. (1998). Predicting U.S. recessions, Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45–61.

Hamilton, J. D. (2018). Why you should never use the Hodrick-Prescott filter. Review of Economics and Statistics, 100(5), 831–843.

Harvey, A. C. (1985). Trends and cycles in macroeconomic time series. Journal of Business & Economic Statistics, 3(3), 216–227.

Harvey, A. C. (1990). Forecasting, structural time series models and the Kalman filter. Cambridge University Press.

Hodrick, R. J., & Prescott, E. C. (1997). Postwar U.S. business cycles, An empirical investigation. Journal of Money, Credit, and Banking, 29(1), 1–16.

Kamber, G., Morley, J., & Wong, B. (2018). Intuitive and reliable estimates of the output gap from a Beveridge-Nelson filter. Review of Economics and Statistics, 100(3), 550–566.

Kamber, G., Morley, J., & Wong, B. (2025). Trend-cycle decomposition in the presence of large shocks. Journal of Economic Dynamics and Control, 173, 105066.

Kauppi, H., & Saikkonen, P. (2008). Predicting U.S. recessions with dynamic binary response models. Review of Economics and Statistics, 90(4), 777–791.

Kishor, N. K., & Nguyen, N. (2025). Measuring the credit gap, A forecast combination approach. Swiss Journal of Economics and Statistics, 161(1), Article 2.

Koenig, E. F., Dolmas, S., & Piger, J. (2003). The use and abuse of real-time data in economic forecasting. Review of Economics and Statistics, 85(3), 618–628.

Marcellino, M., & Musso, A. (2011). The reliability of real-time estimates of the euro area output gap. Economic Modelling, 28(4), 1842–1856.

Marcellino, M., Stock, J. H., & Watson, M. W. (2006). A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. Journal of Econometrics, 135(1–2), 499–526.

Morley, J. C. (2002). A state-space approach to calculating the Beveridge-Nelson decomposition. Economics Letters, 75(1), 123–127.

Newbold, P., & Granger, C. W. J. (1974). Experience with forecasting univariate time series and the combination of forecasts. Journal of the Royal Statistical Society, Series A (General), 137(2), 131–146.

Orphanides, A. (2001). Monetary policy rules based on real-time data. American Economic Review, 91(4), 964–985.

Orphanides, A. (2008). Taylor rules. In The New Palgrave Dictionary of Economics (pp. 1–7). Palgrave Macmillan.

Orphanides, A., & van Norden, S. (2002). The unreliability of output-gap estimates in real time. Review of Economics and Statistics, 84(4), 569–583.

Quast, J., & Wolters, M. H. (2022). Reliable real-time output gap estimates based on a modified Hamilton filter. Journal of Business & Economic Statistics, 40(1), 152–168.

Stock, J. H., & Watson, M. W. (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23(6), 405–430.

Timmermann, A. (2006). Forecast combinations. In G. Elliott, C. W. J. Granger, & A. Timmermann (Eds.), Handbook of Economic Forecasting (Vol. 1, pp. 135–196). Elsevier.