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Forecasting euro area inflation using a huge panel of survey expectations

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  • Huber, Florian
  • Onorante, Luca
  • Pfarrhofer, Michael

Abstract

In this paper, we forecast euro area inflation and its main components using a massive number of time series on survey expectations obtained from the European Commission’s Business and Consumer Survey. To make the estimation of such a huge model tractable, we use recent advances in computational statistics to carry out posterior simulation and inference. Our findings suggest that including a wide range of firms’ and consumers’ opinions about future economic developments offers useful information to forecast prices and assess tail risks to inflation. These predictive improvements arise from surveys related to expected inflation and other questions related to the general economic environment. Finally, we find that firms’ expectations about the future seem to have more predictive content than consumer expectations.

Suggested Citation

  • Huber, Florian & Onorante, Luca & Pfarrhofer, Michael, 2024. "Forecasting euro area inflation using a huge panel of survey expectations," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1042-1054.
  • Handle: RePEc:eee:intfor:v:40:y:2024:i:3:p:1042-1054
    DOI: 10.1016/j.ijforecast.2023.09.003
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    1. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
    4. Moretti, Laura & Onorante, Luca & Zakipour-Saber, Shayan, 2019. "Phillips curves in the euro area," Research Technical Papers 8/RT/19, Central Bank of Ireland.
    5. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    6. Bańbura, Marta & Leiva-León, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Discussion Papers 48/2021, Deutsche Bundesbank.
    7. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
    8. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    9. Sharon Kozicki & P. A. Tinsley, 2012. "Effective Use of Survey Information in Estimating the Evolution of Expected Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 145-169, February.
    10. Ciccarelli, Matteo & García, Juan Angel, 2021. "Expectation spillovers and the return of inflation," Economics Letters, Elsevier, vol. 209(C).
    11. Michael Weber & Francesco D'Acunto & Yuriy Gorodnichenko & Olivier Coibion, 2022. "The Subjective Inflation Expectations of Households and Firms: Measurement, Determinants, and Implications," Journal of Economic Perspectives, American Economic Association, vol. 36(3), pages 157-184, Summer.
    12. Rodolfo Arioli & Colm Bates & Heinz Dieden & Ioana Duca & Roberta Friz & Christian Gayer & Geoff Kenny & Aidan Meyler & Iskra Pavlova, 2016. "EU Consumers’ Quantitative Inflation Perceptions and Expectations: An Evaluation," European Economy - Discussion Papers 038, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    13. Lucia Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon Potter, 2014. "Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 483-500, October.
    14. Claveria, Oscar & Pons, Ernest & Ramos, Raul, 2007. "Business and consumer expectations and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 47-69.
    15. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
    16. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    17. Florian Huber & Gary Koop & Luca Onorante, 2021. "Inducing Sparsity and Shrinkage in Time-Varying Parameter Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 669-683, July.
    18. Bańbura, Marta & Brenna, Federica & Paredes, Joan & Ravazzolo, Francesco, 2021. "Combining Bayesian VARs with survey density forecasts: does it pay off?," Working Paper Series 2543, European Central Bank.
    19. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, June.
    20. Tilmann Gneiting & Roopesh Ranjan, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 411-422, July.
    21. 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.
    22. Chan,Joshua & Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2019. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9781108423380, January.
    23. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    24. Randal J. Verbrugge & Saeed Zaman, 2021. "Whose Inflation Expectations Best Predict Inflation?," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(19), pages 1-7, October.
    25. 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.
    26. 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.
    27. Niko Hauzenberger & Florian Huber & Gary Koop & Luca Onorante, 2022. "Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1904-1918, October.
    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. de Vincent-Humphreys, Rupert & Dimitrova, Ivelina & Falck, Elisabeth & Henkel, Lukas & Meyler, Aidan, 2019. "Twenty years of the ECB Survey of Professional Forecasters," Economic Bulletin Articles, European Central Bank, vol. 1.
    30. 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.
    31. Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021. "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1247-1260.
    32. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    33. Gneiting, Tilmann & Ranjan, Roopesh, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 411-422.
    34. 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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