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Do output gap estimates improve inflation forecasts in Slovakia?

Author

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  • Nataliia Ostapenko

    (National Bank of Slovakia)

Abstract

The paper compares different output gap measures regarding their real-time reliability and usefulness for predicting inflation in Slovakia. The results indicate that estimated cycles from the Modified Hamilton filter, a Mixed-Frequency Bayesian Vector Autoregression and a Dynamic Factor Model are economically reasonable, similar in magnitudes to the official central bank estimate and, more importantly, stable over time. Furthermore, among all compared output gap estimates, the gap from the Mixed-Frequency Vector Autoregression can predict Slovak inflation better than other estimates of the cyclical position until the recent period of high inflation in 2021–2022.

Suggested Citation

  • Nataliia Ostapenko, 2022. "Do output gap estimates improve inflation forecasts in Slovakia?," Working and Discussion Papers WP 4/2022, Research Department, National Bank of Slovakia.
  • Handle: RePEc:svk:wpaper:1088
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    References listed on IDEAS

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    1. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    2. Ludovit Odor & Judita Jurasekova Kucserova, 2014. "Finding Yeti: More robust estimates of output gap in Slovakia," Working Papers Working Paper No. 2/2014, Council for Budget Responsibility.
    3. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
    4. 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.
    5. James Morley & Benjamin Wong, 2020. "Estimating and accounting for the output gap with large Bayesian vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 1-18, January.
    6. Günes Kamber & James Morley & Benjamin Wong, 2018. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 550-566, July.
    7. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    8. Newey, Whitney K & West, Kenneth D, 1987. "Hypothesis Testing with Efficient Method of Moments Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(3), pages 777-787, October.
    9. Marko Melolinna & Máté Tóth, 2019. "Output gaps, inflation and financial cycles in the UK," Empirical Economics, Springer, vol. 56(3), pages 1039-1070, March.
    10. Antonello D’Agostino & Domenico Giannone & Michele Lenza & Michele Modugno, 2016. "Nowcasting Business Cycles: A Bayesian Approach to Dynamic Heterogeneous Factor Models," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 569-594, Emerald Group Publishing Limited.
    11. Guido Ascari & Luca Fosso, 2021. "The Inflation Rate Disconnect Puzzle: On the International Component of Trend Inflation and the Flattening of the Phillips Curve," Working Paper 2021/17, Norges Bank.
    12. Laurence Ball & Sandeep Mazumder, 2021. "A Phillips curve for the euro area," International Finance, Wiley Blackwell, vol. 24(1), pages 2-17, April.
    13. Morley, James C., 2002. "A state-space approach to calculating the Beveridge-Nelson decomposition," Economics Letters, Elsevier, vol. 75(1), pages 123-127, March.
    14. 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.
    15. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    16. Olivier Coibion & Yuriy Gorodnichenko & Mauricio Ulate, 2018. "The Cyclical Sensitivity in Estimates of Potential Output," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 49(2 (Fall)), pages 343-441.
    17. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    18. Fabian Eser & Peter Karadi & Philip R. Lane & Laura Moretti & Chiara Osbat, 2020. "The Phillips Curve at the ECB," Manchester School, University of Manchester, vol. 88(S1), pages 50-85, September.
    19. Chan,Joshua & Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2019. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9781108437493, September.
    20. Kevin J. Lansing, 2019. "Improving the Phillips Curve with an Interaction Variable," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    21. Juraj Hucek & Alexander Karsay & Marian Vavra, 2015. "Short-term Forecasting of Real GDP Using Monthly Data," Working and Discussion Papers OP 1/2015, Research Department, National Bank of Slovakia.
    22. 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.
    23. Joshua C C Chan & Angelia L Grant, 2017. "Measuring the output gap using stochastic model specification search," CAMA Working Papers 2017-02, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    24. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    25. James D. Hamilton, 2017. "Why You Should Never Use the Hodrick-Prescott Filter," NBER Working Papers 23429, National Bureau of Economic Research, Inc.
    26. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    27. 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.
    28. James H. Stock & Mark W. Watson, 2007. "Erratum to “Why Has U.S. Inflation Become Harder to Forecast?”," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    29. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    30. 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.).
    31. Chan,Joshua & Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2019. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9781108437493, September.
    32. Mr. Alvar Kangur & Koralai Kirabaeva & Jean-Marc Natal & Simon Voigts, 2019. "How Informative Are Real Time Output Gap Estimates in Europe?," IMF Working Papers 2019/200, International Monetary Fund.
    33. Anderton, Robert & Elding, Catherine & Haroutunian, Stephan & Jarvis, Valerie & Aranki, Ted & Rusinova, Desislava & Labhard, Vincent & Jacquinot, Pascal & Dieppe, Alistair & Szörfi, Béla, 2014. "Potential output from a euro area perspective," Occasional Paper Series 156, European Central Bank.
    34. Mr. Jiaqian Chen & Lucyna Gornicka, 2020. "Measuring Output Gap: Is It Worth Your Time?," IMF Working Papers 2020/024, International Monetary Fund.
    35. 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.
    36. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    37. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
    38. Mihnea Constantinescu & Anh Dinh Minh Nguyen, 2017. "Unemployment or Credit: Who Holds The Potential? Results From a Small-Open Economy," Bank of Lithuania Discussion Paper Series 4, Bank of Lithuania.
    39. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: 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
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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