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A time varying approach to the stock return–inflation puzzle

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  • Xiaoye Li
  • Zhibiao Zhao

Abstract

In the large literature on the stock return–inflation puzzle, existing works have used constant coefficient linear regression models or change point analysis with abrupt change points. Motivated by the time varying stock return–inflation relationship and the drawbacks of change point analysis, we propose to use the recently emerged locally stationary models to model stock return and inflation. Although the model exhibits non‐parametric time varying dependence structure over a long time span, it has local stationarity within each small time interval. Detailed empirical analysis is conducted and comparisons are made between various approaches. We find that the stock return–inflation correlation is negative during early sample periods and turns positive during late sample periods, but the turning time point is different for the total inflation rate and core inflation rate.

Suggested Citation

  • Xiaoye Li & Zhibiao Zhao, 2019. "A time varying approach to the stock return–inflation puzzle," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(5), pages 1509-1528, November.
  • Handle: RePEc:bla:jorssc:v:68:y:2019:i:5:p:1509-1528
    DOI: 10.1111/rssc.12372
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    Cited by:

    1. Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).
    2. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.

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