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A Statistical Forecasting Method for Inflation Forecasting: Hitting Every Vector Autoregression and Forecasting under Model Uncertainty

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  • Fujiwara, Ippei

    (Bank of Japan)

  • Koga, Maiko

    (Bank of Japan)

Abstract

Typically, when conducting econometric forecasting, estimation is carried out on a forecasting model that is built upon some assumed economic structure. However, such techniques cannot avoid running into the possibility of misspecification, which will occur should there be some error in the assumptions underlying this economic structure. In this paper, in which we concentrate upon inflation forecasting, we present a method of hitting every vector autoregression (VAR) and forecasting under model uncertainty (HEVAR/FMU) that stresses statistical relationships among time-series data, and that makes no structural assumptions, other than to set up the underlying variables. Use of this HEVAR/FMU, in addition to establishing a more objective setting and enabling us to produce forecasts that take uncertainty into account, gives better results when forecasting qualitative movements in inflation. Therefore, we can state that the HEVAR/FMU can also play a valuable role in providing a cross-check for forecasts produced using such structural-type models.

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Bibliographic Info

Article provided by Institute for Monetary and Economic Studies, Bank of Japan in its journal Monetary and Economic Studies.

Volume (Year): 22 (2004)
Issue (Month): 1 (March)
Pages: 123-142

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Handle: RePEc:ime:imemes:v:22:y:2004:i:1:p:123-142

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  1. David Hendry & Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Economics Series Working Papers 3, University of Oxford, Department of Economics.
  2. Pesaran, M.H. & Timmermann, A., 1990. "A Simple Non-Parametric Test Of Predictive Performance," Papers 29, California Los Angeles - Applied Econometrics.
  3. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  4. Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Econometric Society World Congress 2000 Contributed Papers 0411, Econometric Society.
  5. Melick, William R. & Thomas, Charles P., 1997. "Recovering an Asset's Implied PDF from Option Prices: An Application to Crude Oil during the Gulf Crisis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 32(01), pages 91-115, March.
  6. Gonzalo Camba-Mendez & George Kapetanios & Richard J. Smith & Martin Weale, 1999. "Tests of Rank in Reduced Rank Regression Models," NIESR Discussion Papers 150, National Institute of Economic and Social Research.
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Cited by:
  1. Tierney, Heather L.R., 2010. "Real-Time Data Revisions and the PCE Measure of Inflation," MPRA Paper 22387, University Library of Munich, Germany, revised Apr 2010.
  2. Tierney, Heather L.R., 2009. "Examining the Ability of Core Inflation to Capture the Overall Trend of Total Inflation," MPRA Paper 22409, University Library of Munich, Germany, revised Feb 2010.
  3. Tierney, Heather L.R., 2009. "Evaluating Exclusion-from-Core Measures of Inflation using Real-Time Data," MPRA Paper 17856, University Library of Munich, Germany.
  4. Tierney, Heather L.R., 2009. "A Local Examination for Persistence in Exclusions-from-Core Measures of Inflation Using Real-Time Data," MPRA Paper 13089, University Library of Munich, Germany.
  5. Tierney, Heather L.R., 2011. "Forecasting and tracking real-time data revisions in inflation persistence," MPRA Paper 34439, University Library of Munich, Germany.

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