IDEAS home Printed from https://ideas.repec.org/p/zbw/bofecr/62021.html
   My bibliography  Save this paper

Output gaps and cyclical indicators: Finnish evidence

Author

Listed:
  • Pönkä, Harri
  • Sariola, Mikko

Abstract

The output gap is a commonly used tool to assess the state of the business cycle, and as such, a key input for policy makers. In this article, we employ principal components analysis (PCA) to derive an estimate of the output gap in Finland that summarizes the information of widely used cyclical indicators. This methodology produces an output gap that is similar to the ones obtained from the main methods used at the Bank of Finland and the European Commission, but requiring considerably less modelling effort. The method is also flexible and can readily be adopted to internalize additional information that captures special circumstances, such as the current pandemic. In this spirit, we extend our information set to include a service turnover indicator, and find that it clearly improves the method's ability to capture the exceptional downturn in 2020.

Suggested Citation

  • Pönkä, Harri & Sariola, Mikko, 2021. "Output gaps and cyclical indicators: Finnish evidence," BoF Economics Review 6/2021, Bank of Finland.
    • Handle: RePEc:zbw:bofecr:62021
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/243288/1/1773397524.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1113-1141, December.
    2. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Christian Gayer & Bertrand Marc, 2018. "A ‘New Modesty’? Level Shifts in Survey Data and the Decreasing Trend of ‘Normal’ Growth," European Economy - Discussion Papers 083, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    5. Zsolt Darvas & Andras Simon, 2015. "Filling the gap: open economy considerations for more reliable potential output estimates," CERS-IE WORKING PAPERS 1557, Institute of Economics, Centre for Economic and Regional Studies.
    6. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    7. Karel Havik & Kieran Mc Morrow & Fabrice Orlandi & Christophe Planas & Rafal Raciborski & Werner Roeger & Alessandro Rossi & Anna Thum-Thysen & Valerie Vandermeulen, 2014. "The Production Function Methodology for Calculating Potential Growth Rates & Output Gaps," European Economy - Economic Papers 2008 - 2015 535, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    8. Sariola, Mikko, 2019. "An unobserved components model for Finland – Estimates of potential output and NAWRU," BoF Economics Review 2/2019, Bank of Finland.
    9. Werner Roeger & Kieran Mc Morrow & Atanas Hristov & Valerie Vandermeulen, 2019. "Output Gaps and Cyclical Indicator," European Economy - Discussion Papers 104, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    10. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    11. Zsolt Darvas & András Simon, 2015. "Filling the Gap-- Open Economy Considerations for More Reliable Potential Output Estimates," EcoMod2015 8650, EcoMod.
    12. Tóth, Máté, 2021. "A multivariate unobserved components model to estimate potential output in the euro area: a production function based approach," Working Paper Series 2523, European Central Bank.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.).
    2. Michał Brzoza-Brzezina & Jacek Kotłowski, 2009. "Bezwzględna stopa inflacji w gospodarce polskiej," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 9, pages 1-21.
    3. Matteo Barigozzi & Antonio M. Conti & Matteo Luciani, 2014. "Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(5), pages 693-714, October.
    4. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    5. Mario Forni & Luca Gambetti & Luca Sala, 2014. "No News in Business Cycles," Economic Journal, Royal Economic Society, vol. 124(581), pages 1168-1191, December.
    6. Massacci, Daniele, 2017. "Least squares estimation of large dimensional threshold factor models," Journal of Econometrics, Elsevier, vol. 197(1), pages 101-129.
    7. Bai, Jushan & Ando, Tomohiro, 2013. "Multifactor asset pricing with a large number of observable risk factors and unobservable common and group-specific factors," MPRA Paper 52785, University Library of Munich, Germany, revised Dec 2013.
    8. Lorenzo Burlon & Paolo D'Imperio, 2019. "The euro-area output gap through the lens of a DSGE model," Questioni di Economia e Finanza (Occasional Papers) 477, Bank of Italy, Economic Research and International Relations Area.
    9. Simona Delle Chiaie & Laurent Ferrara & Domenico Giannone, 2022. "Common factors of commodity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 461-476, April.
    10. Barigozzi, Matteo & Trapani, Lorenzo, 2020. "Sequential testing for structural stability in approximate factor models," Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 5149-5187.
    11. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    12. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    13. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    14. Romain Houssa & Lasse Bork & Hans Dewachter, 2008. "Identification of Macroeconomic Factors in Large Panels," Working Papers 1010, University of Namur, Department of Economics.
    15. Aboura, Sofiane & Chevallier, Julien, 2015. "Cross-market volatility index with Factor-DCC," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 132-140.
    16. Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.
    17. Van Nieuwenhuyze, Christophe & Benk, Szilard & Rünstler, Gerhard & Cristadoro, Riccardo & Den Reijer, Ard & Jakaitiene, Audrone & Jelonek, Piotr & Rua, António & Ruth, Karsten & Barhoumi, Karim, 2008. "Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise," Occasional Paper Series 84, European Central Bank.
    18. Jacobs, Jan P.A.M. & Otter, Pieter W. & den Reijer, Ard H.J., 2012. "Information, data dimension and factor structure," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 80-91.
    19. Bai, Jushan & Liao, Yuan, 2016. "Efficient estimation of approximate factor models via penalized maximum likelihood," Journal of Econometrics, Elsevier, vol. 191(1), pages 1-18.
    20. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.

    More about this item

    Keywords

    cyclical indicators; output gap; potential output; principal component analysis; service turnover; COVID-19;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:bofecr:62021. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/bofgvfi.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.