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Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches

Author

Listed:
  • Yamin Ahmad

    (University of Wisconsin-Whitewater)

  • Adam Check

    (U.S. Bank, Hopkins Excelsior Blvd)

  • Ming Chien Lo

    (Metropolitan State University, College of Management)

Abstract

We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroeconomic time series with small data. We use a Monte Carlo approach to evaluate the predictions from these approaches and find that ML outperforms both the Classical and Bayesian tests using prediction accuracy, and appears to be the most flexible for classifying unit roots when class imbalance is present. In data, we find broad consensus among the approaches for predicted nonstationary series, with some disagreement for predicted stationary series.

Suggested Citation

  • Yamin Ahmad & Adam Check & Ming Chien Lo, 2024. "Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2139-2173, June.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:6:d:10.1007_s10614-023-10397-0
    DOI: 10.1007/s10614-023-10397-0
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    More about this item

    Keywords

    k-nearest neighbors; Random forest; Supervised learning; Support vector machines;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models

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