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  • Search: subject:"modelling and forecasting"
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Year of publication
Subject
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modelling and forecasting 3 ARFIMA 2 Energy Demand Modelling and Forecasting 2 Future Scenarios 2 Structural Time Series Model (STSM) 2 fGARCH 2 hybrid model 2 sGARCH 2 volatility 2 ARCH model 1 ARCH-Modell 1 ARMA model 1 ARMA-Modell 1 Climate change 1 Commodity derivative 1 Energiepolitik 1 Energiewende 1 Energy policy 1 Energy transition 1 Erneuerbare Energie 1 Estimation 1 Forecasting model 1 Förderung erneuerbarer Energien 1 Klimawandel 1 Nachhaltige Entwicklung 1 Oil price 1 Prognoseverfahren 1 Renewable energy 1 Renewable energy policy 1 Rohstoffderivat 1 Schätzung 1 Sustainable development 1 TIMES model 1 Theorie 1 Theory 1 Time series analysis 1 Turkish Industrial Electricity Demand 1 Turkish Residential Electricity Demand 1 Volatility 1 Volatilität 1
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Online availability
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Free 5 CC license 2
Type of publication
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Article 3 Book / Working Paper 2
Type of publication (narrower categories)
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Article in journal 2 Aufsatz in Zeitschrift 2 Article 1
Language
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English 5
Author
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AlـGounmeein, Remal Shaher 2 Dilaver, Zafer 2 Hunt, Lester C 2 Diachuk, Oleksandr Anatoliiovych 1 Ismail, Mohd Tahir 1 Mohd Tahir Ismail 1 Semeniuk, Andrii Оlehovych 1
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Institution
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Surrey Energy Economics Centre (SEEC), School of Economics 2
Published in...
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Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 2 Economy and forecasting : scientific journal 1 Statistics in Transition New Series 1 Statistics in transition : an international journal of the Polish Statistical Association and Statistics Poland 1
Source
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ECONIS (ZBW) 2 RePEc 2 EconStor 1
Showing 1 - 5 of 5
Cover Image
Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach
AlـGounmeein, Remal Shaher; Ismail, Mohd Tahir - In: Statistics in Transition New Series 22 (2021) 1, pp. 29-54
The Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Autoregressive Conditionally Heteroskedastic (fGARCH) model were applied to study the volatility of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, which...
Persistent link: https://www.econbiz.de/10012600278
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Modelling and forecasting monthly Brent crude oil prices : a long memory and volatility approach
AlـGounmeein, Remal Shaher; Mohd Tahir Ismail - In: Statistics in transition : an international journal of … 22 (2021) 1, pp. 29-54
The Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Autoregressive Conditionally Heteroskedastic (fGARCH) model were applied to study the volatility of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, which...
Persistent link: https://www.econbiz.de/10012487052
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Methods and models of estimating energy transition on the example of Zhytomyr united territorial community
Diachuk, Oleksandr Anatoliiovych; Semeniuk, Andrii … - In: Economy and forecasting : scientific journal (2021) 4, pp. 62-85
Persistent link: https://www.econbiz.de/10013347455
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Modelling and Forecasting Turkish Residential Electricity Demand
Dilaver, Zafer; Hunt, Lester C - Surrey Energy Economics Centre (SEEC), School of Economics - 2010
This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period 1960 to 2008. Household total final...
Persistent link: https://www.econbiz.de/10008833223
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Industrial Electricity Demand for Turkey: A Structural Time Series Analysis
Dilaver, Zafer; Hunt, Lester C - Surrey Energy Economics Centre (SEEC), School of Economics - 2010
This research investigates the relationship between Turkish industrial electricity consumption, industrial value added and electricity prices in order to forecast future Turkish industrial electricity demand. To achieve this, an industrial electricity demand function for Turkey is estimated by...
Persistent link: https://www.econbiz.de/10008674215
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