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Measuring China's core inflation for forecasting purposes: taking persistence as weight

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Abstract

This paper constructs China's core inflation index based on persistence weights for monetary policy and inflation forecast. We apply the univariate autoregression model (AR), whereby the sum of autoregression coefficients is used to measure inflation persistence. Due to the possible unit root, we adopt the grid-bootstrap method to obtain the estimates for the inflation persistence. We re-weight the importance of each CPI component, according to its relative persistence. By comparing with traditional core inflation and headline inflation, we find that the persistence-weighted core inflation, is a leading indicator and an attractor of China's headline inflation, with a strong ability to predict the short-term headline inflation, as well as monetary policy decisions.

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Notes

  1. For example, the core personal consumption expenditure price index computed by the Federal Reserve excludes food and energy prices, and the core inflation index computed by the European Central Bank excludes food, raw material and energy prices.

  2. The autoregression equations of inflation in Europe or US usually contain no more than one or two lags (Bilke and Stracca, 2007).

  3. We extract grid number G = 100, repeat the sampling times B = 999, and obtain the significance of 95% and 5%, respectively.

  4. See Zhang (2020) for more discussion on Chinese consumers’ expenditure patterns across various product categories.

  5. We focus on the close-economy case by using the domestic output gap. See Gadzinski and Hoffmann (2008) for details about the foreign output gap.

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Funding

Penglong Zhang acknowledges the financial support from the National Natural Science Foundation of China (Project IDs: 72003104).

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Authors and Affiliations

  1. School of Economics, Renmin University of China, Beijing, 100084, China

    Zhiyong Fan & Yushan Hu

  2. School of Public Policy and Management, Tsinghua University, Beijing, 100084, China

    Penglong Zhang

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  1. Zhiyong Fan
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  2. Yushan Hu
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  3. Penglong Zhang
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Correspondence to Penglong Zhang.

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Fan, Z., Hu, Y. & Zhang, P. Measuring China's core inflation for forecasting purposes: taking persistence as weight. Empir Econ 63, 93–111 (2022). https://doi.org/10.1007/s00181-021-02128-x

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  • DOI: https://doi.org/10.1007/s00181-021-02128-x

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