IDEAS home Printed from https://ideas.repec.org/p/cbe/wpaper/202301.html
   My bibliography  Save this paper

Closer to Finding Yeti

Author

Listed:
  • Tomas Micko

    (Council for Budget Responsibility)

  • Alexander Karsay

    (Council for Budget Responsibility)

  • Zuzana Mucka

    (Council for Budget Responsibility)

  • Lucia Sramkova

    (Council for Budget Responsibility)

Abstract

This paper offers a synthesis of several approaches to measuring output gap in Slovakia and serves as an update of the original CBR work Finding Yeti after almost a decade. A “suite of models” approach is estimated and assessed to provide advantages over single models. Following the recommendation of the EU IFIs guide suggesting no one-size-fits-all approach for measuring output gap, our family of methods consist of two unobserved component models, principal component model, semi-structural model and Modified Hamilton filter. We propose a novelty approach to weighting the individual models capturing recent structural innovations in the economy to construct one central estimate of the output gap. Such a robust estimate is maximising its overall plausibility and applicability to prudent fiscal policy assessment.

Suggested Citation

  • Tomas Micko & Alexander Karsay & Zuzana Mucka & Lucia Sramkova, 2023. "Closer to Finding Yeti," Working Papers Working Paper No. 1/2023, Council for Budget Responsibility.
    • Handle: RePEc:cbe:wpaper:202301
    as

    Download full text from publisher

    File URL: https://www.rrz.sk/wp-content/uploads/2023/08/2023_08_23_Closer-to-Finding-Yeti_Final.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Planas, Christophe & Rossi, Alessandro & Fiorentini, Gabriele, 2008. "Bayesian Analysis of the Output Gap," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 18-32, January.
    2. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    3. Patrick Blagrave & Mr. Roberto Garcia-Saltos & Mr. Douglas Laxton & Fan Zhang, 2015. "A Simple Multivariate Filter for Estimating Potential Output," IMF Working Papers 2015/079, International Monetary Fund.
    4. Josefine Quast & Maik H. Wolters, 2022. "Reliable Real-Time Output Gap Estimates Based on a Modified Hamilton Filter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 152-168, January.
    5. Alan J. Auerbach, 2009. "Implementing the New Fiscal Policy Activism," American Economic Review, American Economic Association, vol. 99(2), pages 543-549, May.
    6. Michel Juillard & Charles Freedman & Dmitry Korshunov & Mr. Douglas Laxton & Mr. Ondrej Kamenik & Ioan Carabenciov & Igor Ermolaev & Jared Laxton, 2008. "A Small Quarterly Multi-Country Projection Model with Financial-Real Linkages and Oil Prices," IMF Working Papers 2008/280, International Monetary Fund.
    7. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    8. James D. Hamilton, 2018. "Why You Should Never Use the Hodrick-Prescott Filter," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 831-843, December.
    9. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    10. Berger, Tino & Kempa, Bernd, 2011. "Bayesian estimation of the output gap for a small open economy: The case of Canada," Economics Letters, Elsevier, vol. 112(1), pages 107-112, July.
    11. Jared Laxton & Igor Ermolaev & Charles Freedman & Mr. Ondrej Kamenik & Michel Juillard & Mr. Douglas Laxton & Ioan Carabenciov & Dmitry Korshunov, 2008. "A Small Quarterly Multi-Country Projection Model," IMF Working Papers 2008/279, International Monetary Fund.
    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. Chalmovianský, Jakub & Němec, Daniel, 2022. "Assessing uncertainty of output gap estimates: Evidence from Visegrad countries," Economic Modelling, Elsevier, vol. 116(C).
    2. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    3. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    4. Florian Huber & Michael Pfarrhofer & Philipp Piribauer, 2020. "A multi‐country dynamic factor model with stochastic volatility for euro area business cycle analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 911-926, September.
    5. Júlio, Paulo & Maria, José R., 2024. "Trends and cycles during the COVID-19 pandemic period," Economic Modelling, Elsevier, vol. 139(C).
    6. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    7. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    8. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    9. Lars-H. R. Siemers, 2024. "On the Hamilton-HP Filter Controversy: Evidence from German Business Cycles," MAGKS Papers on Economics 202421, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    10. Hertrich Markus, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, De Gruyter, vol. 20(4), pages 759-794, December.
    11. Verbrugge, Randal & Zaman, Saeed, 2023. "The hard road to a soft landing: Evidence from a (modestly) nonlinear structural model," Energy Economics, Elsevier, vol. 123(C).
    12. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).
    13. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    14. Holtemöller, Oliver & Mallick, Sushanta, 2016. "Global food prices and monetary policy in an emerging market economy: The case of India," Journal of Asian Economics, Elsevier, vol. 46(C), pages 56-70.
    15. Patrick Blagrave & Peter Elliott & Mr. Roberto Garcia-Saltos & Mr. Douglas Hostland & Mr. Douglas Laxton & Fan Zhang, 2013. "Adding China to the Global Projection Model," IMF Working Papers 2013/256, International Monetary Fund.
    16. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
    17. Marek Jarociński & Michele Lenza, 2018. "An Inflation‐Predicting Measure of the Output Gap in the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(6), pages 1189-1224, September.
    18. Viv B. Hall & Peter Thomson, 2021. "Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 151-183, November.
    19. Melina Dritsaki & Chaido Dritsaki, 2022. "Comparison of HP Filter and the Hamilton’s Regression," Mathematics, MDPI, vol. 10(8), pages 1-18, April.
    20. Sariola, Mikko, 2019. "An unobserved components model for Finland – Estimates of potential output and NAWRU," BoF Economics Review 2/2019, Bank of Finland.

    More about this item

    Keywords

    output gap; unobserved component; trend; cycle; plausibility; Bayesian analysis; estimation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory

    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:cbe:wpaper:202301. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/rrzbrsk.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.