A robust least square approach for forecasting models: an application to Brazil’s natural gas demand

Costa, Oswaldo Luiz do Valle; Ribeiro, Celma de Oliveira; Ho, Linda Lee; Rego, Erik Eduardo; Parente, Virgínia; Toro, Javier

doi:10.1007/s12667-019-00351-1

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…We, therefore, base the long-term relationship on ROB-L 2 , as used by Costa et al (2019) for forecasting Brazil's natural gas and ARDL techniques are used to check the short-run effects of the variables. The ARDL approach possesses several econometric strengths compared with the conventional time-series data models.…”

Section: Econometric Methodologymentioning

confidence: 99%

Unlocking the investment impact of biomass energy utilization on environmental degradation for an isolated island

Gyamfi

Agozie

Bein

et al. 2021

IJESM

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Purpose Discussions on environment-friendly production connected with the concerns of growing biomass emissions have gained much attention. In this regard, this study aims to explore the issue of biomass energy consumption and its related emission effects on the economic and environmental well-being of the economy of Cyprus. Design/methodology/approach This study sources time series data on specific variables from the Global Material Flow and the World Bank’s World Development Indicators (WDI, 2020) between 1990 and 2016. The Robust least square (ROB-L2) in conjunction with Pesaran autoregressive distributed lag (ARDL) methodology analysis techniques was used in addition to the Granger causality tests to examine the direction of causality flow between the variables under consideration. Findings The results indicate that biomass energy usage in the long run reduces pollution and negatively correlates with CO2 emissions level. Also, the decline of emission is influenced by increased foreign direct investment (FDI), thus, activities of foreign investors contribute to combating emission in the country. According to empirical results, non-renewable energy consumption showed both positive and negative influences on increased emission level, whereas economic growth is increasing carbon dioxide emission for the case of Cyprus. Originality/value This study applies current reliable data that offers renewed insights and sheds light on the state of affairs on biomass utilization from a developing country perspective. Additionally, it extends the discourse on the impact of biomass utilization on CO2 emissions by considering the impact of FDI, trade flow and energy consumption in a carbon-income function built on the liner version of the environmental Kuznets curve hypothesis. Although this is by no means exhaustive, the study pioneers the discourse on how FDI with biomass utilization among other relevant variables influences carbon dioxide emission.

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Section: Econometric Methodologymentioning

confidence: 99%

Unlocking the investment impact of biomass energy utilization on environmental degradation for an isolated island

Gyamfi

Agozie

Bein

et al. 2021

IJESM

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“…Based on the forecast, the authors propose alternative energy policies. Robust least square method combined with log-linear Cobb-Douglas model has been proposed by [15]. The authors compared the proposed robust and ordinary least square methods for the yearly forecast of the natural gas demand in Brazil, considering the total demand as well as the industrial and power sectors demand.…”

Section: Related Workmentioning

confidence: 99%

A hybrid approach for high precision prediction of gas flows

et al. 2021

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About 23% of the German energy demand is supplied by natural gas. Additionally, for about the same amount Germany serves as a transit country. Thereby, the German network represents a central hub in the European natural gas transport network. The transport infrastructure is operated by transmissions system operators (TSOs). The number one priority of the TSOs is to ensure the security of supply. However, the TSOs have only very limited knowledge about the intentions and planned actions of the shippers (traders). Open Grid Europe (OGE), one of Germany’s largest TSO, operates a high-pressure transport network of about 12,000 km length. With the introduction of peak-load gas power stations, it is of great importance to predict in- and out-flow of the network to ensure the necessary flexibility and security of supply for the German Energy Transition (“Energiewende”). In this paper, we introduce a novel hybrid forecast method applied to gas flows at the boundary nodes of a transport network. This method employs an optimized feature selection and minimization. We use a combination of a FAR, LSTM and mathematical programming to achieve robust high-quality forecasts on real-world data for different types of network nodes.

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“…The LS method yields superior results and presents reasonable long-term elasticity concerning natural resource demand for energy consumption forecasts [39]. The LS method is extensively employed in forecasting in Asia, including China [40][41][42][43][44], Turkey [45], and Korea [46,47].…”

Section: Introductionmentioning

confidence: 99%

Accuracy Rate of Least Square Support Vector Machine Method and Its Various Modifications: A Forecasting Evaluation on Multi-Type Data

Mahsup,

Febriani,

Syaharuddin

et al. 2024

ISI

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A robust least square approach for forecasting models: an application to Brazil’s natural gas demand

Cited by 7 publications

References 29 publications

Unlocking the investment impact of biomass energy utilization on environmental degradation for an isolated island

Unlocking the investment impact of biomass energy utilization on environmental degradation for an isolated island

A hybrid approach for high precision prediction of gas flows

Accuracy Rate of Least Square Support Vector Machine Method and Its Various Modifications: A Forecasting Evaluation on Multi-Type Data

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