Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks

Jeronymo Marcondes Pinto () and Jennifer Castle
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Jeronymo Marcondes Pinto: Secretariat of Labour Inspection

Journal of Business Cycle Research, 2022, vol. 18, issue 2, No 1, 129-157

Abstract: Abstract Forecasting economic indicators is an important task for analysts. However, many indicators suffer from structural breaks leading to forecast failure. Methods that are robust following a structural break have been proposed in the literature but they come at a cost: an increase in forecast error variance. We propose a method to select between a set of robust and non-robust forecasting models. Our method uses time-series clustering to identify possible structural breaks in a time series, and then switches between autoregressive forecasting models depending on the series dynamics. We perform a rigorous empirical evaluation with 400 simulated series with an artificial structural break and with real data economic series: Industrial Production and Consumer Prices for all Western European countries available from the OECD database. Our results show that the proposed method statistically outperforms benchmarks in forecast accuracy for most case scenarios, particularly at short horizons.

Keywords: Machine learning; Forecasting; Structural breaks; Model selection; Cluster analysis (search for similar items in EconPapers)
JEL-codes: C52 C53 C87 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s41549-022-00066-w

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