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“Density forecasts of inflation using Gaussian process regression models”

Author

Listed:
  • Petar Soric

    (University of Zagreb)

  • Enric Monte

    (Polytechnic University of Catalunya)

  • Salvador Torra

    (Riskcenter-IREA, University of Barcelona)

  • Oscar Claveria

    (AQR-IREA, University of Barcelona)

Abstract

The present study uses Gaussian Process regression models for generating density forecasts of inflation within the New Keynesian Phillips curve (NKPC) framework. The NKPC is a structural model of inflation dynamics in which we include the output gap, inflation expectations, fuel world prices and money market interest rates as predictors. We estimate country-specific time series models for the 19 Euro Area (EA) countries. As opposed to other machine learning models, Gaussian Process regression allows estimating confidence intervals for the predictions. The performance of the proposed model is assessed in a one-step-ahead forecasting exercise. The results obtained point out the recent inflationary pressures and show the potential of Gaussian Process regression for forecasting purposes.

Suggested Citation

  • Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. "“Density forecasts of inflation using Gaussian process regression models”," AQR Working Papers 202207, University of Barcelona, Regional Quantitative Analysis Group, revised Jul 2022.
    • Handle: RePEc:aqr:wpaper:202207
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    References listed on IDEAS

    as
    1. Tallman, Ellis W. & Zaman, Saeed, 2017. "Forecasting inflation: Phillips curve effects on services price measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 442-457.
    2. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    3. Olivier Coibion & Yuriy Gorodnichenko & Rupal Kamdar, 2018. "The Formation of Expectations, Inflation, and the Phillips Curve," Journal of Economic Literature, American Economic Association, vol. 56(4), pages 1447-1491, December.
    4. Gali, Jordi & Gertler, Mark, 1999. "Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 195-222, October.
    5. Kirstin Hubrich & Frauke Skudelny, 2017. "Forecast Combination for Euro Area Inflation: A Cure in Times of Crisis?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 515-540, August.
    6. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688.
    7. Marianna Riggi & Fabrizio Venditti, 2015. "Failing to Forecast Low Inflation and Phillips Curve Instability: A Euro-Area Perspective," International Finance, Wiley Blackwell, vol. 18(1), pages 47-68, March.
    8. Luca Fanelli, 2008. "Testing the New Keynesian Phillips Curve Through Vector Autoregressive Models: Results from the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(1), pages 53-66, February.
    9. Sérgio Lagoa, 2017. "Determinants of inflation differentials in the euro area: Is the New Keynesian Phillips Curve enough?," Journal of Applied Economics, Universidad del CEMA, vol. 20, pages 75-103, May.
    10. Tomislav Globan & Vladimir Arčabić & Petar Sorić, 2016. "Inflation in New EU Member States: A Domestically or Externally Driven Phenomenon?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(1), pages 154-168, January.
    11. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
    12. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
    13. A. W. Phillips, 1958. "The Relation Between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861–1957," Economica, London School of Economics and Political Science, vol. 25(100), pages 283-299, November.
    14. Sérgio Lagoa, 2017. "Determinants of Inflation Differentials in the Euro Area: Is the New Keynesian Phillips Curve Enough?," Journal of Applied Economics, Taylor & Francis Journals, vol. 20(1), pages 75-103, May.
    15. Hindrayanto, Irma & Samarina, Anna & Stanga, Irina M., 2019. "Is the Phillips curve still alive? Evidence from the euro area," Economics Letters, Elsevier, vol. 174(C), pages 149-152.
    16. Sophocles Mavroeidis & Mikkel Plagborg-Møller & James H. Stock, 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Journal of Economic Literature, American Economic Association, vol. 52(1), pages 124-188, March.
    17. Raffaella Giacomini, 2015. "Economic theory and forecasting: lessons from the literature," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 22-41, June.
    18. Melisso Boschi & Alessandro Girardi, 2007. "Euro area inflation: long-run determinants and short-run dynamics," Applied Financial Economics, Taylor & Francis Journals, vol. 17(1), pages 9-24.
    19. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    20. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.
    21. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    22. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    23. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    24. Maritta Paloviita, 2006. "Inflation Dynamics in the Euro Area and the Role of Expectations," Empirical Economics, Springer, vol. 31(4), pages 847-860, November.
    25. Chortareas, Georgios & Magonis, George & Panagiotidis, Theodore, 2012. "The asymmetry of the New Keynesian Phillips Curve in the euro-area," Economics Letters, Elsevier, vol. 114(2), pages 161-163.
    26. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Combination forecasts of tourism demand with machine learning models," Applied Economics Letters, Taylor & Francis Journals, vol. 23(6), pages 428-431, April.
    27. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
    28. Galí, Jordi & Gertler, Mark, 1999. "Inflation Dynamics: A Structural Economic Analysis," CEPR Discussion Papers 2246, C.E.P.R. Discussion Papers.
    29. Bonam, Dennis & de Haan, Jakob & van Limbergen, Duncan, 2021. "Time-varying wage Phillips curves in the euro area with a new measure for labor market slack," Economic Modelling, Elsevier, vol. 96(C), pages 157-171.
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    More about this item

    Keywords

    Machine learning; Gaussian process regression; Time-series analysis; Economic forecasting; Inflation; New Keynesian Phillips curve JEL classification: C45; C51; C53; E31;
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