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Forecasting gasoline prices with mixed random forest error correction models

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorEscribano, Álvaro
dc.contributor.authorWang, Dandan
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Economíaes
dc.date.accessioned2020-06-04T14:59:00Z
dc.date.available2020-06-04T14:59:00Z
dc.date.issued2020-06-04
dc.description.abstractThe 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.en
dc.description.sponsorshipThe 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 comments received in the workshop on “40 years of Cointegration” organized by FUNCAS where a preliminary version of this paper was presenteden
dc.identifier.issn2340-5031es
dc.identifier.urihttps://hdl.handle.net/10016/30557
dc.identifier.uxxiDT/0000001759es
dc.language.isoenges
dc.relation.ispartofseriesWorking paper. Economicsen
dc.relation.ispartofseries20-06
dc.relation.projectIDGobierno de España. ECO2016-00105-001es
dc.relation.projectIDGobierno de España. MDM 2014-0431es
dc.relation.projectIDComunidad de Madrid. MadEco-CM S2015/HUM-3444es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.jelB23
dc.subject.jelC24
dc.subject.jelC52
dc.subject.jelC53
dc.subject.jelD43
dc.subject.jelL13
dc.subject.jelL71
dc.subject.otherForecasting Gasoline Pricesen
dc.subject.otherRockets And Feathers Hypothesisen
dc.subject.otherCointegrationen
dc.subject.otherNonlinear Error Correctionen
dc.subject.otherMachine Learningen
dc.subject.otherRandom Foresten
dc.subject.otherMixed Random Foresten
dc.titleForecasting gasoline prices with mixed random forest error correction modelsen
dc.typeworking paper*
dspace.entity.typePublication
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