Citas bibligráficas
Colan, L., (2021). Modelo gaussiano y probabilístico de multivariable para estimar el efecto de precios futuros de gas en proyectos de inversión convencional [Tesis, Universidad Nacional de Ingeniería]. http://hdl.handle.net/20.500.14076/21918
Colan, L., Modelo gaussiano y probabilístico de multivariable para estimar el efecto de precios futuros de gas en proyectos de inversión convencional [Tesis]. PE: Universidad Nacional de Ingeniería; 2021. http://hdl.handle.net/20.500.14076/21918
@mastersthesis{renati/711423,
title = "Modelo gaussiano y probabilístico de multivariable para estimar el efecto de precios futuros de gas en proyectos de inversión convencional",
author = "Colan García, Luis Alberto",
publisher = "Universidad Nacional de Ingeniería",
year = "2021"
}
The global growth of gas has a dynamic impact on extractive activities in different regions of the world. However, in recent decades the price of gas has shown obvious falls, not as much as those of oil, which generates certain uncertainty about its future trend, which could raise questions about whether projects are viable in the near future. Estimating future gas price forecasts is of utmost relevance for projects in the market at the global and national level even as it allows decision-making with less uncertainty. However, it is not always easy to have a model that allows estimating prices efficiently because it is subject to multiple variables or factors including production, consumption, among others also reserves and exports, which are decisive in estimating the price of gas. From this, in this study, a diagnosis of the determining factors in price prediction is developed through descriptive and probabilistic statistical applications to subsequently determine the correlation of price behavior, regression model and forecast for a period 2020-2030. The predictions of the regression models found for a future period 2020-2030 of the analysis of prices as a function of time and projecting it over time show an upward trend, however the regression models with variables project a tendency to decrease during the future period. 2020-2030.
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