IDEAS home Printed from https://ideas.repec.org/a/spr/cejnor/v31y2023i4d10.1007_s10100-023-00851-7.html
   My bibliography  Save this article

Estimation of the potential GDP by a new robust filter method

Author

Listed:
  • Éva Gyurkovics

    (Budapest University of Technology and Economics)

  • Tibor Takács

    (Corvinus University of Budapest)

Abstract

The first purpose of this paper is to propose a theoretically new robust filter method to estimate non-observable macroeconomic indicators. The second purpose is to apply the proposed method to estimate the Hungarian potential GDP in 2000–2021. The novelty of the proposed filter method is that — unlike papers published so far — it does not require the stability of the dynamic model, only a partial stability condition must be satisfied. Moreover, such time-dependent uncertainties and nonlinearities can arise in the model that satisfy a general quadratic constraint. An important advantage of the proposed robust filter method over the traditional Kalman filter is that no stochastic assumptions is needed that may not be valid for the problem at hand. The proposed filter method has never been applied to estimate the potential GDP. To estimate the Hungarian potential GDP, the proposed method is applied using uni-, bi- and trivariate models. Estimations up to 2021 has not been published yet for the Hungarian economy. The examined period includes both the financial world crisis and the Covid-19 crisis. The results of the different models are consistent. It turned out that the economic policy was very procyclical after 2012, and the GDP gap was still positive during and also after the Covid-19 crisis.

Suggested Citation

  • Éva Gyurkovics & Tibor Takács, 2023. "Estimation of the potential GDP by a new robust filter method," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(4), pages 1183-1207, December.
    • Handle: RePEc:spr:cejnor:v:31:y:2023:i:4:d:10.1007_s10100-023-00851-7
    DOI: 10.1007/s10100-023-00851-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10100-023-00851-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10100-023-00851-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Perron, Pierre & Wada, Tatsuma, 2009. "Let's take a break: Trends and cycles in US real GDP," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 749-765, September.
    2. Tetsuya Konuki, 2010. "Estimating Potential Output and the Output Gap in Slovakia," Eastern European Economics, Taylor & Francis Journals, vol. 48(2), pages 39-55, March.
    3. 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.
    4. Marko Melolinna & Máté Tóth, 2019. "Output gaps, inflation and financial cycles in the UK," Empirical Economics, Springer, vol. 56(3), pages 1039-1070, March.
    5. Pierre St-Amant & Simon van Norden, 1997. "Measurement of the Output Gap: A Discussion of Recent Research at the Bank of Canada," Technical Reports 79, Bank of Canada.
    6. Andrew Evans, 2018. "Okun coefficients and participation coefficients by age and gender," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 7(1), pages 1-22, December.
    7. James C. Morley & Charles R. Nelson & Eric Zivot, 2003. "Why Are the Beveridge-Nelson and Unobserved-Components Decompositions of GDP So Different?," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 235-243, May.
    8. Fabio Busetti & Michele Caivano, 2016. "The trend–cycle decomposition of output and the Phillips curve: Bayesian estimates for Italy and the Euro area," Empirical Economics, Springer, vol. 50(4), pages 1565-1587, June.
    9. Peter K. Clark, 1987. "The Cyclical Component of U. S. Economic Activity," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 102(4), pages 797-814.
    10. Arabinda Basistha, 2007. "Trend‐cycle correlation, drift break and the estimation of trend and cycle in Canadian GDP," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 40(2), pages 584-606, May.
    11. Nicolas A. Cuche & Martin K. Hess, 1999. "Estimating Monthly GDP In A General Kalman Filter Framework: Evidence From Switzerland," Working Papers 99.02, Swiss National Bank, Study Center Gerzensee.
    12. 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.
    13. Kuttner, Kenneth N, 1994. "Estimating Potential Output as a Latent Variable," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 361-368, July.
    14. Enders, Walter & Li, Jing, 2015. "Trend-cycle decomposition allowing for multiple smooth structural changes in the trend of US real GDP," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 71-81.
    15. Harvey, A C, 1985. "Trends and Cycles in Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(3), pages 216-227, June.
    16. Gyurkovics, Éva & Takács, Tibor, 2022. "Robust energy-to-peak filter design for a class of unstable polytopic systems with a macroeconomic application," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    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. Gyurkovics, Éva & Takács, Tibor, 2022. "Robust energy-to-peak filter design for a class of unstable polytopic systems with a macroeconomic application," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    2. Tino Berger, 2011. "Estimating Europe’s natural rates," Empirical Economics, Springer, vol. 40(2), pages 521-536, April.
    3. Cayen, Jean-Philippe & van Norden, Simon, 2005. "The reliability of Canadian output-gap estimates," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 373-393, December.
    4. Max Soloschenko & Enzo Weber, 2021. "Trend-Cycle Interactions and the Subprime Crisis: Analysis of US and Canadian Output," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 109-128, November.
    5. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    6. Angelia L. Grant & Joshua C.C. Chan, 2017. "A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 525-552, March.
    7. Arabinda Basistha, 2007. "Trend‐cycle correlation, drift break and the estimation of trend and cycle in Canadian GDP," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 40(2), pages 584-606, May.
    8. T. Berger, 2008. "Estimating Europe’s Natural Rates from a forward-looking Phillips curve," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/498, Ghent University, Faculty of Economics and Business Administration.
    9. Ángel Guillén & Gabriel Rodríguez, 2014. "Trend-cycle decomposition for Peruvian GDP: application of an alternative method," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 23(1), pages 1-44, December.
    10. James Morley & Irina B. Panovska & Tara M. Sinclair, 2013. "Testing Stationarity for Unobserved Components Models," Discussion Papers 2012-41A, School of Economics, The University of New South Wales.
    11. Manuel González-Astudillo & John M. Roberts, 2022. "When are trend–cycle decompositions of GDP reliable?," Empirical Economics, Springer, vol. 62(5), pages 2417-2460, May.
    12. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 363-393, March.
    13. Marian Vavra, 2016. "Testing the Validity of Assumptions of UC-ARIMA Models for Trend-Cycle Decompositions," Working and Discussion Papers WP 4/2016, Research Department, National Bank of Slovakia.
    14. Luis Uzeda, 2022. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 25-53, Emerald Group Publishing Limited.
    15. Blonigen, Bruce A. & Piger, Jeremy & Sly, Nicholas, 2014. "Comovement in GDP trends and cycles among trading partners," Journal of International Economics, Elsevier, vol. 94(2), pages 239-247.
    16. Castillo, Luis & Florián, David, 2019. "Measuring the output gap, potential output growth and natural interest rate from a semi-structural dynamic model for Peru," Working Papers 2019-012, Banco Central de Reserva del Perú.
    17. Tobias Hartl & Rolf Tschernig & Enzo Weber, 2020. "Fractional trends and cycles in macroeconomic time series," Papers 2005.05266, arXiv.org, revised May 2020.
    18. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    19. Yu-Fan Huang & Sui Luo, 2018. "Potential output and inflation dynamics after the Great Recession," Empirical Economics, Springer, vol. 55(2), pages 495-517, September.
    20. Buncic, Daniel, 2020. "Econometric issues with Laubach and Williams’ estimates of the natural rate of interest," Working Paper Series 397, Sveriges Riksbank (Central Bank of Sweden).

    More about this item

    Keywords

    Potential GDP; Robust filtering; Polytopic and quadratically bounded uncertainties; Linear matrix inequality; Unobserved components model; Trend-cycle decomposition;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:spr:cejnor:v:31:y:2023:i:4:d:10.1007_s10100-023-00851-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.