#4591. Mixed random forest, cointegration, and forecasting gasoline prices

August 2026publication date
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Business and International Management;
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Abstract:
One of the most successful forecasting machine learning (ML) procedures is random forest (RF). In this paper, we propose a new mixed RF approach for modelling departures from linearity that helps identify (i) explanatory variables with nonlinear impacts, (ii) threshold values, and (iii) the closest parametric approximation. The methodology is applied to weekly forecasts of gasoline prices, cointegrated with international oil prices and exchange rates. Recent specifications for nonlinear error correction (NEC) models include threshold autoregressive models (TAR) and double-threshold smooth transition autoregressive (STAR) models. We propose a new mixed RF model specification strategy and apply it to the determinants of weekly prices of the gasoline market from 20XX to 20XX. In particular, the mixed RF is able to identify nonlinearities in both the error correction term and the rate of change of oil prices.
Keywords:
Cointegration; Machine learning; Mixed random forest; Nonlinear error correction; Oil prices; Random forest; Rockets and feathers

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