Modeling and Forecast of Ghana’s GDP Using ARIMA-GARCH Model

Barbara, Dwumah; Li, Chenlong; Ying-chuan, Jing; Samuel, Aning

doi:10.4236/oalib.1108335

Cited by 3 publications

(4 citation statements)

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“…According to the existing research results and historical data of various factors, assume the following data [8,9,10,11] . The index parameters are shown in Table 1.…”

Section: Research Methods 21 Data Sourcementioning

confidence: 99%

Development Status and Carbon Dioxide Emission Forecast of Power Industry in Guangxi

et al. 2023

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This paper uses the Long-range Energy Alternatives Planning(leap) system model to predict the carbon dioxide emissions of the power industry and calculate the carbon peak platform period of the power industry in Guangxi, which is based on the development status of the power industry and the energy and power development plan in Guangxi during the "14th Five-Year Plan". The results show that the power structure of Guangxi is at an excellent level in the country. In 2021, the installed proportion of clean energy is 55.1% (the national average level of 44.7% in 2020), and the proportion of thermal power generation is 55.7% (the national average level of 68.5% in 2020). With the strong economic recovery , the demand for electricity in Guangxi will continue to increase. By 2025 and 2030, the electricity consumption of the whole society will reach 2.9 ×1012 kW•h and 3.7×1012 kW•h, respectively, with an average annual growth rate of about 7.1% and 5.3%. The installed power generation capacity will reach 9.4 ×107 kW and 1.5 ×108 kW, respectively. The power generation will reach 3.0 ×1012 kW•h and 4.2×1012 kW•h electricity, respectively. However, Due to the influence of resource endowment, meteorological conditions, energy storage technology and other constraints on non-fossil energy power generation, it is difficult to achieve large-scale replacement of traditional energy power generation in a short term. It is estimated that coal power production will account for 45% and 30% of the total power generation respectively in Guangxi by 2025 and 2030. Coal power will continue to be the main provider of power security for a long time in the future. Therefore, according to the current situation of power development planning, it is estimated that the carbon emission of Guangxi will reach 9.2×107 t by 2030. The carbon peak platform period of power generation industry will appear in 2029 to 2032, with a peak value of 1.1 × 108 t.

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“…According to the existing research results and historical data of various factors, assume the following data [8,9,10,11] . The index parameters are shown in Table 1.…”

Section: Research Methods 21 Data Sourcementioning

confidence: 99%

Development Status and Carbon Dioxide Emission Forecast of Power Industry in Guangxi

et al. 2023

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“…Barbara, et al, [5], in their research on modeling and forecast of Ghana's GDP used ARIMA-GARCH model approach of time series analysis.. Their paper analysed a combination of both linear and non-linear time series models in making forecast of Ghana's GDP. The data used in the analysis were from 1980 to 2019 on Ghana's GDP current prices.…”

Section: Original Research Articlementioning

confidence: 99%

“…which can be simplified further as (5) The general behaviour of the ACF and PACF for ARMA/ARIMA models is summarized in Table 1 [13].…”

Section: Autoregressive Integrated Moving Average (Arima) Modelmentioning

confidence: 99%

Forecast Analysis of Ghana’s Gross Domestic Product in Economic Growth using Time Series ARIMA

Borbor

Brew

2022

ARJOM

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This paper analyses Ghana’s gross domestic product using time series Autoregressive Integrated Moving Average (ARIMA). Time series analysis involves the application of statistical models to time series data and is useful for analysing the dynamics of Gross domestic product. The Ghana’s Gross domestic products (GDP) from 1980 to 2020 were obtained from the International Monetary Fund (IMF) datasets. Box Jenkins’s methodology of time series analysis was employed to analyse the data. The autocorrelation function (ACF) and partial autocorrelation function (PACF) plot suggested an Autoregression of order one AR(1). The (ARIMA) models were obtained using the minimum AIC criteria. Model diagnostics tests were performed using Ljung-Box test. The paper established that Ghana’s GDP will incline throughout the period of 2021-2025.

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“…Thus, the First Bank of Nigeria's returns series was fully modelled using the ARMA (2, 2)-ARCH (1) model. [13] investigate on Modeling and Forecast of Ghana's GDP Using ARIMA-GARCH Model and concluded that the ARIMA-GARCH model is effective in removing the error variance and improving forecasts. In terms of performance, shows that the combined model outperforms the classic ARIMA model and suggested that further research might investigate the efficiency and accuracy of the ARIMA-GARCH model.…”

Section: Introductionmentioning

confidence: 99%

Heteroscedasticity Detection in the existence of Outliers in Discrete-Time Series

Adehi,

Maijamaa,

Abubakar

et al. 2024

Int. J. Res. Publ. Rev.

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Modeling and Forecast of Ghana’s GDP Using ARIMA-GARCH Model

Cited by 3 publications

References 0 publications

Development Status and Carbon Dioxide Emission Forecast of Power Industry in Guangxi

Development Status and Carbon Dioxide Emission Forecast of Power Industry in Guangxi

Forecast Analysis of Ghana’s Gross Domestic Product in Economic Growth using Time Series ARIMA

Heteroscedasticity Detection in the existence of Outliers in Discrete-Time Series

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