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Estimating the output gap in real time: A factor model approach

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  • Aastveit, Knut Are
  • Trovik, Tørres

Abstract

By using a dynamic factor model, we can substantially improve the reliability of real-time output gap estimates for the U.S. economy. First, we use a factor model to extract a series for the common component in GDP from a large panel of monthly real-time macroeconomic variables. This series is immune to revisions to the extent that revisions are due to unbiased measurement errors or idiosyncratic news. Second, our model is able to handle the unbalanced arrival of the data. This yields favorable nowcasting properties and thus starting conditions for the filtering of data into a trend and deviations from a trend. Combined with the method of augmenting data with forecasts prior to filtering, this greatly reduces the end-of-sample imprecision in the gap estimate. The increased precision has economic importance for real-time policy decisions and improves real-time inflation forecasts.

Suggested Citation

  • Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
  • Handle: RePEc:eee:quaeco:v:54:y:2014:i:2:p:180-193
    DOI: 10.1016/j.qref.2013.09.003
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    Cited by:

    1. Francesco Furlanetto & Kåre Hagelund & Frank Hansen & Ørjan Robstad, 2020. "Norges Bank Output Gap Estimates: Forecasting Properties, Reliability and Cyclical Sensitivity," Working Paper 2020/7, Norges Bank.
    2. Matteo Barigozzi & Matteo Luciani, 2017. "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series 2017-111, Board of Governors of the Federal Reserve System (U.S.).
    3. Florian Eckert & Samad Sarferaz, 2019. "Agnostic Output Gap Estimation and Decomposition in Large Cross-Sections," KOF Working papers 19-467, KOF Swiss Economic Institute, ETH Zurich.
    4. Olivér Miklós Rácz, 2012. "Using confidence indicators for the assessment of the cyclical position of the economy," MNB Bulletin (discontinued), Magyar Nemzeti Bank (Central Bank of Hungary), vol. 7(2), pages 41-46, June.
    5. Hasenzagl, Thomas & Pellegrino, Filippo & Reichlin, Lucrezia & Ricco, Giovanni, 2022. "Monitoring the Economy in Real Time: Trends and Gaps in Real Activity and Prices," CEPR Discussion Papers 17111, C.E.P.R. Discussion Papers.
    6. Knut Aastveit & Tørres Trovik, 2012. "Nowcasting norwegian GDP: the role of asset prices in a small open economy," Empirical Economics, Springer, vol. 42(1), pages 95-119, February.
    7. Travis J. Berge, 2020. "Time-varying Uncertainty of the Federal Reserve’s Output Gap Estimate," Finance and Economics Discussion Series 2020-012r1, Board of Governors of the Federal Reserve System (U.S.), revised 14 Apr 2021.
    8. Mellár, Tamás & Németh, Kristóf, 2018. "A kibocsátási rés becslése többváltozós állapottérmodellekben. Szuperhiszterézis és további empirikus eredmények [Estimating output gap in multivariate state space models. Super-hysteresis and furt," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 557-591.
    9. Fornaro, Paolo, 2016. "Predicting Finnish economic activity using firm-level data," International Journal of Forecasting, Elsevier, vol. 32(1), pages 10-19.
    10. Juan Manuel Julio, 2011. "Data Revisions and the Output Gap," BORRADORES DE ECONOMIA 007956, BANCO DE LA REPÚBLICA.
    11. Paolo Fornaro & Henri Luomaranta, 2020. "Nowcasting Finnish real economic activity: a machine learning approach," Empirical Economics, Springer, vol. 58(1), pages 55-71, January.
    12. Ademmer, Martin & Boysen-Hogrefe, Jens & Carstensen, Kai & Hauber, Philipp & Jannsen, Nils & Kooths, Stefan & Rossian, Thies & Stolzenburg, Ulrich, 2019. "Schätzung von Produktionspotenzial und -lücke: Eine Analyse des EU-Verfahrens und mögliche Verbesserungen," Open Access Publications from Kiel Institute for the World Economy 193965, Kiel Institute for the World Economy (IfW).
    13. Ademmer, Martin & Boysen-Hogrefe, Jens & Carstensen, Kai & Hauber, Philipp & Jannsen, Nils & Kooths, Stefan & Rossian, Thies & Stolzenburg, Ulrich, 2019. "Schätzung von Produktionspotenzial und -lücke: Eine Analyse des EU-Verfahrens und mögliche Verbesserungen," Kieler Beiträge zur Wirtschaftspolitik 19, Kiel Institute for the World Economy (IfW Kiel).
    14. Alessandro Barbarino & Travis J. Berge & Han Chen & Andrea Stella, 2020. "Which Output Gap Estimates Are Stable in Real Time and Why?," Finance and Economics Discussion Series 2020-102, Board of Governors of the Federal Reserve System (U.S.).

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    More about this item

    Keywords

    Output gap; Real time analysis; Monetary policy; Forecasting; Factor model;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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