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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

Decision makers, both public and private, use forecasts of economic growth and inflation to make plans and implement policies. In many situations, reasonably good forecasts can be made with simple rules of thumb that are extrapolations of a single data series. In principle, information about other economic indicators should be useful in forecasting a particular series like inflation or output. Including too many variables makes a model unwieldy and not including enough can increase forecast error. A key problem is deciding which other series to include. Recently, studies have shown that Dynamic Factor Models (DFMs) may provide a general solution to this problem. The key is that these models use a large data set to extract a few common factors (thus, the term #data-rich*). This paper uses a monthly DFM model to forecast inflation and output growth at horizons of 3, 12 and 24 months ahead. These forecasts are then compared to simple forecasting rules.

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Bibliographic Info

Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2006-054.

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Date of creation: 2006
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Publication status: Published in Federal Reserve Bank of St. Louis Review, May/June 2008, 90(3, Part 1), pp. 175-92
Handle: RePEc:fip:fedlwp:2006-054

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Keywords: Inflation (Finance) ; Forecasting;

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  1. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
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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. Kevin L. Kliesen, 2008. "Oil and the U.S. macroeconomy: an update and a simple forecasting exercise," Review, Federal Reserve Bank of St. Louis, issue Sep, pages 505-516.
  3. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.
  4. 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.
  5. Marlene Amstad & Simon Potter, 2009. "Real time underlying inflation gauges for monetary policymakers," Staff Reports 420, Federal Reserve Bank of New York.
  6. Pang, Iris Ai Jao, 2010. "Forecasting Hong Kong economy using factor augmented vector autoregression," MPRA Paper 32495, University Library of Munich, Germany.

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