IDEAS home Printed from https://ideas.repec.org/p/nbp/nbpmis/200.html

Output gap measure based on survey data

Author

Listed:
  • Michał Hulej
  • Grzegorz Grabek

Abstract

Following Nyman (2010), the paper provides an indicator of resource utilisation (RU) for the Polish economy based on survey and labour market data. The indicator is subsequently used to identify output gap. Using real-time dataset, we find that output gap constructed in this way is revised to a similar or (in recent years) lesser extent than a measure based on the Hodrick and Prescott filter and structural approach. Also, the output gap based on the RU indicator performs comparably to other approaches as a proxy of inflation pressure: real-time data evaluation exercise reveals that RMSE of Phillips curve inflation forecasts with the RU indicator-based output gap is similar to the RMSE of equivalent specifications with alternative gap measures.

Suggested Citation

  • Michał Hulej & Grzegorz Grabek, 2015. "Output gap measure based on survey data," NBP Working Papers 200, Narodowy Bank Polski.
    • Handle: RePEc:nbp:nbpmis:200
    as

    Download full text from publisher

    File URL: https://static.nbp.pl/publikacje/materialy-i-studia/200_en.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Claudio BorioBy & Piti Disyatat & Mikael Juselius, 2017. "Rethinking potential output: embedding information about the financial cycle," Oxford Economic Papers, Oxford University Press, vol. 69(3), pages 655-677.
    2. Katarzyna Budnik & Michal Greszta & Michal Hulej & Marcin Kolasa & Karol Murawski & Michal Rot & Bartosz Rybaczyk & Magdalena Tarnicka, 2009. "The new macroeconometric model of the Polish economy," NBP Working Papers 62, Narodowy Bank Polski.
    3. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    4. 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.
    5. Clark, Todd E. & McCracken, Michael W., 2006. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1127-1148, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohamed A. M. Sallam & Mohamed R. Neffati, 2019. "Estimation and Analysis of the Output Gap for the Saudi Economy; Econometric Study (1970-2016)," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(2), pages 267-284, February.
    2. Marcell Göttert & Timo Wollmershäuser, 2021. "Survey-Based Structural Budget Balances," CESifo Working Paper Series 8911, CESifo.
    3. Julián Caballero & Michael Chui & Emanuel Kohlscheen & Christian Upper, 2023. "Inflation and labour markets," BIS Papers chapters, in: Bank for International Settlements (ed.), Inflation and labour markets, volume 127, pages 1-19, Bank for International Settlements.
    4. Maciej Stefański, 2023. "Natural Rate of Interest in a Small Open Economy with Application to CEE Countries," KAE Working Papers 2023-093, Warsaw School of Economics, Collegium of Economic Analysis.

    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. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    2. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    3. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    4. Mayer, Walter J. & Liu, Feng & Dang, Xin, 2017. "Improving the power of the Diebold–Mariano–West test for least squares predictions," International Journal of Forecasting, Elsevier, vol. 33(3), pages 618-626.
    5. Luke Hartigan & Tom Rosewall, 2025. "Nowcasting Quarterly GDP Growth During the COVID‐19 Crisis Using a Monthly Activity Indicator," The Economic Record, The Economic Society of Australia, vol. 101(335), pages 456-484, December.
    6. Maung, Kenwin & Swanson, Norman R., 2025. "A survey of models and methods used for forecasting when investing in financial markets," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1355-1382.
    7. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 1-33.
    8. Todd E. Clark & Michael W. McCracken, 2009. "Combining Forecasts from Nested Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 303-329, June.
    9. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    10. Boriss Siliverstovs & Daniel Wochner, 2019. "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," KOF Working papers 19-463, KOF Swiss Economic Institute, ETH Zurich.
    11. Paulo Maio, 2025. "Does Dividend Policy Lead the Economy?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 57(8), pages 2287-2308, December.
    12. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    13. Bahar Şen Doğan & Murat Midiliç, 2019. "Forecasting Turkish real GDP growth in a data-rich environment," Empirical Economics, Springer, vol. 56(1), pages 367-395, January.
    14. Clark, Todd E. & McCracken, Michael W., 2009. "Tests of Equal Predictive Ability With Real-Time Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 441-454.
    15. Graham Elliott & Allan Timmermann, 2016. "Forecasting in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 81-110, October.
    16. Sekkel, Rodrigo M., 2015. "Balance sheets of financial intermediaries: Do they forecast economic activity?," International Journal of Forecasting, Elsevier, vol. 31(2), pages 263-275.
    17. Hodula Martin & Pfeifer Lukáš, 2018. "Fiscal-Monetary-Financial Stability Interactions in a Data-Rich Environment," Review of Economic Perspectives, Sciendo, vol. 18(3), pages 195-224, September.
    18. O'Brien, Martin & Velasco, Sofia, 2020. "Unobserved components models with stochastic volatility for extracting trends and cycles in credit," Research Technical Papers 09/RT/20, Central Bank of Ireland.
    19. Edmond Berisha & David Gabauer & Rangan Gupta & Chi Keung Marco Lau, 2021. "Time-varying influence of household debt on inequality in United Kingdom," Empirical Economics, Springer, vol. 61(4), pages 1917-1933, October.
    20. Christian Schulz, 2007. "Forecasting economic growth for Estonia : application of common factor methodologies," Bank of Estonia Working Papers 2007-09, Bank of Estonia, revised 04 Sep 2007.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    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:nbp:nbpmis:200. 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: Jakub Growiec (email available below). General contact details of provider: https://edirc.repec.org/data/nbpgvpl.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.