Unit roots in macroeconomic time series: a comparison of classical, Bayesian and machine learning approaches

摘要: 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.