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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. Pesaran, M.H. & Timmermann, A., 1990. "A Simple Non-Parametric Test Of Predictive Performance," Papers 29, California Los Angeles - Applied Econometrics.
  2. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  3. 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.
  4. 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.
  5. Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Econometric Society World Congress 2000 Contributed Papers 0411, Econometric Society.
  6. n/a, 2001. "A Comparison of Personal Sector Saving Rates in the UK, US and Italy," 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. Cheolbeom Park & Sookyung Park, 2013. "Exchange Rate Predictability and a Monetary Model with Time-varying Cointegration Coefficients," Discussion Paper Series 1302, Institute of Economic Research, Korea University.
  3. Tierney, Heather L.R., 2011. "Forecasting and tracking real-time data revisions in inflation persistence," MPRA Paper 34439, 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 13383, University Library of Munich, Germany, revised 03 Feb 2009.
  5. Heather L. R. Tierney, 2012. "Examining the ability of core inflation to capture the overall trend of total inflation," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 493-514, February.
  6. Tierney, Heather L.R., 2009. "Evaluating Exclusion-from-Core Measures of Inflation using Real-Time Data," MPRA Paper 17856, University Library of Munich, Germany.

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