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Could regression of stationary series be spurious?

Author

Listed:
  • Wing-Keung Wong

    (Department of Finance, Fintech Center, and Big Data Research Center, Asia University; Department of Medical Research, China Medical University Hospital, Taiwan; Department of Economics and Finance, The Hang Seng University of Hong Kong)

  • Yushan Cheng

    (School of Mathematics and Statistics, Xi’an Jiaotong University, China)

  • Mu Yue

    (Engineering Systems and Design, Singapore University of Technology & Design)

Abstract

Most of the literature on spurious regression has found that regression of independent and (nearly) non-stationary time series could result in spurious outcomes. Very few studies address the issue of whether regression of stationary time series could also result in spurious outcomes and the study is not comprehensive and thorough. To bridge the gap in the literature, we first conjecture that regression of stationary time series could also result in spurious outcomes. We then examine whether the conjecture holds by providing a comprehensive and thorough study. We further provide a remedy algorithm to correct the spurious problem and improve the interpretability of the model. Extensive simulations are carried out to support our conjecture and demonstrate the effectiveness of the remedy. To demonstrate the applicability of our proposed approach and address the issue of spuriousness, we conduct a numerical analysis and demonstrate the usefulness of our proposed remedy algorithm.

Suggested Citation

  • Wing-Keung Wong & Yushan Cheng & Mu Yue, 2025. "Could regression of stationary series be spurious?," Economic Growth Centre Working Paper Series 2503, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    • Handle: RePEc:nan:wpaper:2503
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    File URL: https://web.hss.ntu.edu.sg/egc/wp/2025/2025-03.pdf
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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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