Modeling and Forecasting Africas GDP with Time Series Models

Uwimana, Annie; Bi, Xiuchun; Zhang, Shuguang

doi:10.29322/ijsrp.8.4.2018.p7608

Cited by 10 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The forecast value of GDP for the next 5 (five) years is significant in the 95% level of confidence interval (Wabomba, Mutwiri, & Fredrick, 2016). From the study of Uwimana, Xiuchun, and Shuguang (2018) on the major economy of Africa has been forecasted GDP by using the ARIMA model. Data has been collected from the year 1990 to 2016 and the model is used to forecast the future value of GDP to 2030 and the result shows that an increasing GDP growth where the average speed of the economy of Africa will be of 5.52% and the GDP could achieve $2185.21 billion to $10186.18 billion.…”

Section: Literature Reviewmentioning

confidence: 99%

Economic Scratch on the Future Growth Path of Bangladesh: An ARIMA Approach

Ferdous

2022

5050

View full text Add to dashboard Cite

The purpose of this study is to investigate the future growth path (growth rate) of Bangladesh. Bangladesh is one of the fastest-growing nations in the world. To secure sustainable and spontaneous economic development, contentious increasing economic growth is a prerequisite. In this paper, Autoregressive Integrated Moving Average (ARIMA) model is used to track down the future growth path of Bangladesh. Time series data from year 1972 to 2018 has been utilize to predict the economic growth for year 2019 to 2028. By using the ARIMA approach it has been found that next 10 years (2019-2028) the GDP growth of Bangladesh will be reduced from the current growth rate. The average GDP growth rate from the year 2019-2026 will be clustered near 5% and in the year 2027 it will be peak by 8.77% and the following year growth rate will be consolidated by 1.39%.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Economic Scratch on the Future Growth Path of Bangladesh: An ARIMA Approach

Ferdous

2022

5050

View full text Add to dashboard Cite

show abstract

“…Economists such as Camcho and Martinez-Martin have developed a single index US business cycle dynamic factor model originally developed by Aruba and Diebold in 2010 to forecast real GDP growth rate in the US. Using time series modelling, Africa's GDP in 20 countries over the period 1990 to 2016 proved an "increasing GDP growth rate where average speed of the economy of Africa will be approximately 5.52% and the GDP could hover between $2185.21 billion and $ 101861.18 billion" (Uwimana et al 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

“…-10000 -5000 0 5000 10000 ARIMA (1, 1, 1) Residual -Saudi Arabia, one-step 19681973197819831988199319982003200820132018 Year Figure 12. Time Series Plot of ARIMA (1, 1, 1) Residuals -Saudi Arabia Figures 13 and 14 of PACF and ACF for the residuals are flat which indicates all information has been captured.…”

Section: Saudi Arabia Modelmentioning

confidence: 99%

Forecasting the GDP per Capita for Egypt and Saudi Arabia Using ARIMA Models

Eissa

2020

RWE

View full text Add to dashboard Cite

Annual time series data is used to forecast GDP per capita using the Box-Jenkins Autoregressive-Integrated Moving-Average (ARIMA) model for the Egyptian and Saudi Arabian economies. The fitted ARIMA model is tested for per capita GDP forecasting of Egypt and of Saudi Arabia for the next ten years. Conclusions convey that the most accurate statistical model as in previous literature that forecast GDP per capita for Egypt and for Saudi Arabia is ARIMA (1,1,2) and ARIMA (1,1,1) respectively. The diagnostic tests reveal that the two models presented individually are both stable and reliable.

show abstract

“…With the aid of data from 1980 to 2013, Dritsaki (2015) modeled and forecasted Greece's real GDP using ARIMA. Uwimana et al (2018) modeled the GDP of 20 African countries using data from 1990 to 2016. Noura (2020) forecasted the GDP per Capita for Egypt and Saudi Arabia using ARIMA models.…”

Section: Introductionmentioning

confidence: 99%

On Forecasting Nigeria’s GDP: A Comparative Performance of Regression with ARIMA Errors and ARIMA Method

Ugoh¹,

Echebiri²,

Temisan³

et al. 2022

IJMSS

View full text Add to dashboard Cite

This paper examines the application of autoregressive integrated moving average (ARIMA) model and regression model with ARIMA errors for forecasting Nigeria’s GDP. The data used in this study are collected from the official website of World Bank for the period 1990-2019. A response variable (GDP) and four predictor variables are used for the study. The ARIMA model is fitted only to the response variable, while regression with ARIMA errors is fitted on the data as a whole. The Akaike Information Criterion Corrected (AICc) was used to select the best model among the selected ARIMA models, while the best model for forecasting GDP is selected using measures of forecast accuracy. The result showed that regression with ARIMA(2,0,1) errors is the best model for forecasting Nigeria’s GDP.

show abstract

Modeling and Forecasting Africas GDP with Time Series Models

Cited by 10 publications

References 0 publications

Economic Scratch on the Future Growth Path of Bangladesh: An ARIMA Approach

Economic Scratch on the Future Growth Path of Bangladesh: An ARIMA Approach

Forecasting the GDP per Capita for Egypt and Saudi Arabia Using ARIMA Models

On Forecasting Nigeria’s GDP: A Comparative Performance of Regression with ARIMA Errors and ARIMA Method

Contact Info

Product

Resources

About