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A two factor model to combine US inflation forecasts

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Author Info
Pilar Poncela
Eva Senra
Abstract

The combination of individual forecasts is often a useful tool to improve forecast accuracy. The most commonly used technique for forecast combination is the mean, and it has frequently proved hard to surpass. This study considers factor analysis to combine US inflation forecasts showing that just one factor is not enough to beat the mean and that the second one is necessary. The first factor is usually a weighted mean of the variables and it can be interpreted as a consensus forecast, while the second factor generally provides the differences among the variables and, since the observations are forecasts, it may be related with the dispersion in forecasting expectations and, in a sense, with its uncertainty. Within this approach, the study also revisits Friedman's hypothesis relating the level of inflation with expectations uncertainty at the beginning of the twenty-first century.

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Article provided by Taylor and Francis Journals in its journal Applied Economics.

Volume (Year): 38 (2006)
Issue (Month): 18 (October)
Pages: 2191-2197
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Handle: RePEc:taf:applec:v:38:y:2006:i:18:p:2191-2197

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  1. Yeung Lewis Chan & James H. Stock & Mark W. Watson, 1999. "A dynamic factor model framework for forecast combination," Spanish Economic Review, Springer, vol. 1(2), pages 91-121. [Downloadable!] (restricted)
  2. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  3. Batchelor, Roy, 2001. "How Useful Are the Forecasts of Intergovernmental Agencies? The IMF and OECD versus the Consensus," Applied Economics, Taylor and Francis Journals, vol. 33(2), pages 225-35, February. [Downloadable!] (restricted)
  4. A. Kontonikas, 2002. "Inflation and Inflation Uncertainty in the United Kingdom: Evidence from GARCH modelling," Public Policy Discussion Papers 02-28, Economics and Finance Section, School of Social Sciences, Brunel University. [Downloadable!]
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  5. Batchelor, Roy & Dua, Pami, 1996. "Empirical Measures of Inflation Uncertainty: A Cautionary Note," Applied Economics, Taylor and Francis Journals, vol. 28(3), pages 333-41, March. [Downloadable!] (restricted)
  6. Caporale, Tony & McKiernan, Barbara, 1997. "High and variable inflation: Further evidence on the Friedman hypothesis," Economics Letters, Elsevier, vol. 54(1), pages 65-68, January. [Downloadable!] (restricted)
  7. Cukierman, Alex & Wachtel, Paul, 1979. "Differential Inflationary Expectations and the Variability of the Rate of Inflation: Theory and Evidence," American Economic Review, American Economic Association, vol. 69(4), pages 595-609, September. [Downloadable!] (restricted)
  8. Massimiliano Marcellino & Carlo A. Favero & Francesca Neglia, 2005. "Principal components at work: the empirical analysis of monetary policy with large data sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 603-620. [Downloadable!]
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  9. Engsted, T, 1991. "A Note on the Rationality of Survey Inflation Expectations in the United Kingdom," Applied Economics, Taylor and Francis Journals, vol. 23(7), pages 1269-75, July.
  10. Victor Zarnowitz & Phillip Braun, 1994. "Twenty-two Years of the NBER-ASA Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance," NBER Working Papers 3965, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  11. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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