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Forecasting inflation and output: comparing data-rich models with simple rules

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Author Info

  • William T. Gavin
  • Kevin L. Kliesen

Abstract

There has been a resurgence of interest in dynamic factor models for use by policy advisors. Dynamic factor methods can be used to incorporate a wide range of economic information when forecasting or measuring economic shocks. This article introduces dynamic factor models that underlie the data-rich methods and also tests whether the data-rich models can help a benchmark autoregressive model forecast alternative measures of inflation and real economic activity at horizons of 3, 12, and 24 months ahead. The authors find that, over the past decade, the data-rich models significantly improve the forecasts for a variety of real output and inflation indicators. For all the series that they examine, the authors find that the data-rich models become more useful when forecasting over longer horizons. The exception is the unemployment rate, where the principal components provide significant forecasting information at all horizons.

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Bibliographic Info

Article provided by Federal Reserve Bank of St. Louis in its journal Review.

Volume (Year): (2008)
Issue (Month): May ()
Pages: 175-192

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Handle: RePEc:fip:fedlrv:y:2008:i:may:p:175-192:n:v.90no.3,pt.1

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Related research

Keywords: Inflation (Finance) ; Economic forecasting;

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References

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Citations

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Cited by:
  1. Craig S. Hakkio, 2009. "Global inflation dynamics," Research Working Paper RWP 09-01, Federal Reserve Bank of Kansas City.
  2. Marlene Amstad & Simon Potter, 2009. "Real time underlying inflation gauges for monetary policymakers," Staff Reports 420, Federal Reserve Bank of New York.
  3. Ziegler, Christina & Eickmeier, Sandra, 2006. "How good are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Discussion Paper Series 1: Economic Studies 2006,42, Deutsche Bundesbank, Research Centre.
  4. Kevin L. Kliesen, 2008. "Oil and the U.S. macroeconomy: an update and a simple forecasting exercise," Working Papers 2008-009, Federal Reserve Bank of St. Louis.
  5. Pang, Iris Ai Jao, 2010. "Forecasting Hong Kong economy using factor augmented vector autoregression," MPRA Paper 32495, University Library of Munich, Germany.
  6. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.

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