Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286171 
Title (translated): 
Use of hierarchical models to find the best model to forecast the gallons of regular gasoline demanded
Year of Publication: 
2019
Citation: 
[Journal:] Revista de Métodos Cuantitativos para la Economía y la Empresa [ISSN:] 1886-516X [Volume:] 28 [Year:] 2019 [Pages:] 113-123
Publisher: 
Universidad Pablo de Olavide, Sevilla
Abstract (Translated): 
The objective of this analysis is to find the best hierarchical model to forecast the total demand for regular gasoline in Bogotá, Colombia and, therefore, the collection of gasoline surcharges, which is an important tax used to finance road networks and massive transportation systems. We used data reported by 6 wholesalers of regular gasoline in the city, and used two univariate approaches (ARIMA and exponential smoothing (ETS)), five methods and different minimization algorithms to forecast gallons of regular gasoline. Results show that the best combination of these parameters is an ETS model under a simple univariate forecast.
Subjects: 
Colombia
gasoline
hierarchical models
time series
forecasts
JEL: 
C22
C53
H2
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-sa Logo
Document Type: 
Article

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