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Changing anchor of the renminbi: A Bayesian learning approach to the decade-long transition

Author

Listed:
  • Chen Zhang
  • Ying Fang
  • Linlin Niu

Abstract

China’s exchange rate reform, initiated in 2005, had the goal of switching from a fixed U.S. dollar (USD) peg to a floating mechanism with reference to a trade-weighted currency basket. Over a decade of gradual transition, the renminbi (RMB) has gained importance in the international monetary system and has shown higher flexibility in its dollar value. However, previous studies have demonstrated the inertia of the RMB in maintaining a de facto dollar peg, with little evidence of the up-to-date effectiveness of the official currency basket. We present a Bayesian time-varying coefficient regression on the currency peg or basket weight suitable for studying transition process. We show that the “8.11” reform in 2015 triggered an eventual anchor switching, driving down the dollar weight from 1 to around 0.3. Since 2016, the weight of the official basket has been double that of the USD. SVAR and TVP-VAR analysis, controlling for endogeneity, provide consistent evidence of this regime change, which has important implications for China and the global economy.

Suggested Citation

  • Chen Zhang & Ying Fang & Linlin Niu, 2022. "Changing anchor of the renminbi: A Bayesian learning approach to the decade-long transition," Working Papers 2022-08-24, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
  • Handle: RePEc:wyi:wpaper:002607
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    References listed on IDEAS

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    More about this item

    Keywords

    Renminbi; exchange rate regime; dollar peg; currency basket;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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