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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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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 26 (2010)
Issue (Month): 4 (October)
Pages: 808-835

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Handle: RePEc:eee:intfor:v:26:y::i:4:p:808-835

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Output growth forecasts Inflation forecasts Model selection Structural change Forecast evaluation Real-time data;

References

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  1. Rossi, Barbara, 2002. "Optimal Tests for Nested Model Selection with Underlying Parameter Instability," Working Papers 02-05, Duke University, Department of Economics.
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Citations

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Cited by:
  1. Kai Carstensen & Klaus Wohlrabe & Christina Ziegler, 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), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 82-106, February.
  2. Barbara Rossi & Tatevik Sehkposyan, 2013. "Evaluating Predictive Densities of U.S. Output Growth and Inflation in a Large Macroeconomic Data Set," Working Papers 689, Barcelona Graduate School of Economics.
  3. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank, Research Department.
  4. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2012. "Forecasting UK GDP growth, inflation and interest rates under structural change: a comparison of models with time-varying parameters," Bank of England working papers 450, Bank of England.
  5. 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.
  6. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," MPRA Paper 39452, University Library of Munich, Germany.
  7. 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 1120-54, December.
  8. Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
  9. 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.

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