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Improving inflation prediction with the quantity theory

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  • Wang, Ying
  • Tu, Yundong
  • Chen, Song Xi

Abstract

This paper focuses on the role of the quantity theory in improving inflation forecasts. We find that the cointegration-based quantity theory does not hold for the period after 1995 for the U.S. data. However, that period is well explained by an adaptive quantity theory based on a functional-coefficient cointegration that adapts to the unemployment rate. The forecasting exercises show that the adaptive quantity theory has superior predictive power for targeting future inflation.

Suggested Citation

  • Wang, Ying & Tu, Yundong & Chen, Song Xi, 2016. "Improving inflation prediction with the quantity theory," Economics Letters, Elsevier, vol. 149(C), pages 112-115.
    • Handle: RePEc:eee:ecolet:v:149:y:2016:i:c:p:112-115
    DOI: 10.1016/j.econlet.2016.10.023
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    References listed on IDEAS

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    Cited by:

    1. Kumhof, Michael & Wang, Xuan, 2021. "Banks, money, and the zero lower bound on deposit rates," Journal of Economic Dynamics and Control, Elsevier, vol. 132(C).
    2. Malgorzata Solarz & Jacek Adamek, 2021. "Factors Affecting Mobile Banking Adoption in Poland: An Empirical Study," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1018-1046.
    3. Zhenzhong Wang & Yundong Tu & Song Xi Chen, 2019. "Analyzing China's Consumer Price Index Comparatively with that of United States," Papers 1910.13301, arXiv.org.

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    More about this item

    Keywords

    Cointegrations; Inflation forecasting; Quantity theory of money; Phillips Curve;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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