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

Author

Listed:
  • 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.

Suggested Citation

  • Fujiwara, Ippei & Koga, Maiko, 2004. "A Statistical Forecasting Method for Inflation Forecasting: Hitting Every Vector Autoregression and Forecasting under Model Uncertainty," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 22(1), pages 123-142, March.
    • Handle: RePEc:ime:imemes:v:22:y:2004:i:1:p:123-142
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    References listed on IDEAS

    as
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    Cited by:

    1. 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.
    2. Tierney, Heather L.R., 2011. "Forecasting and tracking real-time data revisions in inflation persistence," MPRA Paper 34439, University Library of Munich, Germany.
    3. Tierney, Heather L.R., 2011. "Real-time data revisions and the PCE measure of inflation," Economic Modelling, Elsevier, vol. 28(4), pages 1763-1773, July.
    4. Park, Cheolbeom & Park, Sookyung, 2013. "Exchange rate predictability and a monetary model with time-varying cointegration coefficients," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 394-410.
    5. 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.
    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.
    7. Mihaela Bratu, 2013. "New accuracy measures for point and interval forecasts. A case study for Romania’s forecasts of inflation and unemployment rate," Economic Analysis Working Papers (2002-2010). Atlantic Review of Economics (2011-2016), Colexio de Economistas de A Coruña, Spain and Fundación Una Galicia Moderna, vol. 1, pages 1-1, June.
    8. Heather L. R. Tierney, 2019. "Forecasting with the Nonparametric Exclusion-from-Core Inflation Persistence Model Using Real-Time Data," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(1), pages 39-63, February.

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    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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