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Have economic models' forecasting performance for US output growth and inflation changed over time, and when?

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  • Rossi, Barbara
  • Sekhposyan, Tatevik

Abstract

We evaluate various economic models' relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the models' relative performance can vary over time. We show that the models' relative performance have, in fact, changed dramatically over time, for both revised and real-time data, and investigate possible factors that might explain such changes. In addition, this paper establishes two empirical stylized facts. Specifically, most predictors for output growth lost their predictive ability in the mid-1970s, and became essentially useless over the last two decades. When forecasting inflation, on the other hand, fewer predictors are significant, and their predictive ability worsened significantly around the time of the Great Moderation.

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  • Rossi, Barbara & Sekhposyan, Tatevik, 2010. "Have economic models' forecasting performance for US output growth and inflation changed over time, and when?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 808-835, October.
  • Handle: RePEc:eee:intfor:v:26:y::i:4:p:808-835
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    Cited by:

    1. Hännikäinen, Jari, 2015. "Zero lower bound, unconventional monetary policy and indicator properties of interest rate spreads," Review of Financial Economics, Elsevier, vol. 26(C), pages 47-54.
    2. Rossi, Barbara & Sekhposyan, Tatevik, 2014. "Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set," International Journal of Forecasting, Elsevier, vol. 30(3), pages 662-682.
    3. González-Rivera, Gloria & Sun, Yingying, 2017. "Density forecast evaluation in unstable environments," International Journal of Forecasting, Elsevier, vol. 33(2), pages 416-432.
    4. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    5. Carlos P. Barros & Luis A. Gil-Alana, 2013. "Inflation Forecasting in Angola: A Fractional Approach," African Development Review, African Development Bank, vol. 25(1), pages 91-104, March.
    6. Serena Ng & Jonathan H. Wright, 2013. "Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1120-1154, December.
    7. Bel, Koen & Paap, Richard, 2016. "Modeling the impact of forecast-based regime switches on US inflation," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1306-1316.
    8. Bel, K. & Paap, R., 2013. "Modeling the impact of forecast-based regime switches on macroeconomic time series," Econometric Institute Research Papers EI 2013-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E, 2016. "Priors for the Long Run," CEPR Discussion Papers 11261, C.E.P.R. Discussion Papers.
    10. Carstensen Kai & Wohlrabe Klaus & Ziegler Christina, 2011. "Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 82-106, February.
    11. Rodrigo Sekkel, 2014. "Balance Sheets of Financial Intermediaries: Do They Forecast Economic Activity?," Staff Working Papers 14-40, Bank of Canada.
    12. Benjamin Beckers & Konstantin A. Kholodilin & Dirk Ulbricht, 2017. "Reading between the Lines: Using Media to Improve German Inflation Forecasts," Discussion Papers of DIW Berlin 1665, DIW Berlin, German Institute for Economic Research.
    13. Linlin Niu & Xiu Xu & Ying Chen, 2015. "An Adaptive Approach to Forecasting Three Key Macroeconomic Variables for Transitional China," SFB 649 Discussion Papers SFB649DP2015-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    14. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    15. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, Elsevier.
    16. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    17. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," MPRA Paper 39452, University Library of Munich, Germany.
    18. Sekkel, Rodrigo M., 2015. "Balance sheets of financial intermediaries: Do they forecast economic activity?," International Journal of Forecasting, Elsevier, vol. 31(2), pages 263-275.
    19. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    20. Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
    21. Harun Özkan & M. Yazgan, 2015. "Is forecasting inflation easier under inflation targeting?," Empirical Economics, Springer, vol. 48(2), pages 609-626, March.
    22. Mawuli Segnon & Rangan Gupta & Stelios Bekiros & Mark E. Wohar, 2016. "Forecasting US GNP Growth: The Role of Uncertainty," Working Papers 201667, University of Pretoria, Department of Economics.
    23. Anna Florio, 2016. "The central bank as shaper and observer of events: The case of the yield spread," Canadian Journal of Economics, Canadian Economics Association, vol. 49(1), pages 320-346, February.
    24. repec:bof:bofitp:urn:nbn:fi:bof-201504131155 is not listed on IDEAS
    25. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.

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