RT Generic
T1 Forecasting gasoline prices with mixed random forest error correction models
A1 Escribano, Álvaro
A1 Wang, Dandan
A2 Universidad Carlos III de Madrid. Departamento de Economía, 
AB The use of machine learning (ML) models has been shown to have advantages over alternative and more traditional time series models in the presence of big data. One of the most successful ML forecasting procedures is the Random Forest (RF) machine learning algorithm. In this paper we propose a mixed RF approach for modeling departures from linearity, instead of starting with a completely nonlinear or nonparametric model. The methodology is applied to the weekly forecasts of gasoline prices that are cointegrated with international oil prices and exchange rates. The question of interest is whether gasoline prices react asymmetrically to increases in oil prices rather than to decreases in oil prices, the "rockets and feathers" hypothesis. In this literature most authors estimate parametric nonlinear error correction models using nonlinear least squares. Recent specifications for nonlinear error correction models include threshold autoregressive models (TAR), double threshold error correction models (ECM) or double threshold smooth transition autoregressive (STAR) models. In this paper, we describe the econometric methodology that combines linear dynamic autoregressive distributed lag (ARDL) models with cointegrated variables with added nonlinear components, or price asymmetries, estimated by the powerful tool of RF. We apply our mixed RF specification strategy to weekly prices of the Spanish gasoline market from 2010 to 2019. We show that the new mixed RF error correction model has important advantages over competing parametric and nonparametric models, in terms of the generality of model specification, estimation and forecasting.
SN 2340-5031
YR 2020
FD 2020-06-04
LK https://hdl.handle.net/10016/30557
UL https://hdl.handle.net/10016/30557
LA eng
NO The first author acknowledges the funding received from the Ministry of Economics of Spain(ECO2016-00105-001, MDM 2014-0431), the Community of Madrid (MadEco-CM S2015/HUM-3444) and the Agencia Estatal de Investigación (2019/00419/001) as well as the commentsreceived in the workshop on “40 years of Cointegration” organized by FUNCAS where apreliminary version of this paper was presented
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RD 1 may. 2024