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Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?

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  • Manzan, Sebastiano
  • Zerom, Dawit

Abstract

Much of the US inflation forecasting literature deals with examining the ability of macroeconomic indicators to predict the mean of future inflation, and the overwhelming evidence suggests that the macroeconomic indicators provide little or no predictability. In this paper, we expand the scope of inflation predictability and explore whether macroeconomic indicators are useful in predicting the distribution of future inflation. To incorporate macroeconomic indicators into the prediction of the conditional distribution of future inflation, we introduce a semi-parametric approach using conditional quantiles. The approach offers more flexibility in capturing the possible role of macroeconomic indicators in predicting the different parts of the future inflation distribution. Using monthly data on US inflation, we find that unemployment rate, housing starts, and the term spread provide significant out-of-sample predictability for the distribution of core inflation. Importantly, this result is obtained for a forecast evaluation period that we intentionally chose to be after 1984, when current research shows that macroeconomic indicators do not add much to the predictability of the future mean inflation. This paper discusses various findings using forecast intervals and forecast densities, and highlights the unique insights that the distribution approach offers, which otherwise would be ignored if we relied only on mean forecasts.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 14387.

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Date of creation: 30 Jan 2009
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Handle: RePEc:pra:mprapa:14387

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Keywords: Conditional quantiles; Distribution; Inflation; Predictability; Phillips curve; Combining;

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  1. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," CREATES Research Papers 2010-21, School of Economics and Management, University of Aarhus.
  2. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, Econometric Society, vol. 78(3), pages 1093-1125, 05.
  3. Hong, Yongmiao & Li, Haitao & Zhao, Feng, 2007. "Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates," Journal of Econometrics, Elsevier, Elsevier, vol. 141(2), pages 736-776, December.
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  5. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, Econometric Society, vol. 74(6), pages 1545-1578, November.
  6. Jonas D. M. Fisher & Chin Te Liu & Ruilin Zhou, 2002. "When can we forecast inflation?," Economic Perspectives, Federal Reserve Bank of Chicago, Federal Reserve Bank of Chicago, issue Q I, pages 32-44.
  7. Kilian, Lutz & Manganelli, Simone, 2007. "The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan," CEPR Discussion Papers, C.E.P.R. Discussion Papers 6031, C.E.P.R. Discussion Papers.
  8. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
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  10. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 25, pages 177-190, April.
  11. Andrew Ang & Geert Bekaert & Min Wei, 2006. "Do macro variables, asset markets, or surveys forecast inflation better?," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2006-15, Board of Governors of the Federal Reserve System (U.S.).
  12. Clark, Todd E. & McCracken, Michael W., 2006. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 38(5), pages 1127-1148, August.
  13. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, Elsevier, vol. 44(2), pages 293-335, October.
  14. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.
  15. Andrew Atkeson & Lee E. Ohanian., 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
  16. Valentina Corradi & Norman Swanson, 2004. "Predective Density and Conditional Confidence Interval Accuracy Tests," Departmental Working Papers, Rutgers University, Department of Economics 200423, Rutgers University, Department of Economics.
  17. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, Econometric Society, vol. 46(1), pages 33-50, January.
  18. Alan Greenspan, 2004. "Risk and Uncertainty in Monetary Policy," American Economic Review, American Economic Association, American Economic Association, vol. 94(2), pages 33-40, May.
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Cited by:
  1. Barbara Rossi & Tatevik Sehkposyan, 2013. "Evaluating Predictive Densities of US Output Growth and Inflation in a Large Macroeconomic Data Set," Working Papers 689, Barcelona Graduate School of Economics.
  2. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers, Duke University, Department of Economics 11-20, Duke University, Department of Economics.

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