Nasir Saleem,Atif Akbar,A. H. M. Rahmatullah Imon,Abu Sayed Md Al Mamun,




AIC,Linear Trend Model,Time Series Models,Gross,Domestic Product,Forecasting,


The purpose of this study was to forecast the Gross Domestic Product (GDP) of Pakistan. GDP of Pakistan was observed and analyzed by using time series analysis techniques and Box-Jenkins methodology. These methods were used for analysis, estimation, and forecasting purposes. Data of GDP of Pakistan was collected from (1961-2020). The model selected had the lowest Akaike Information Criteria (AIC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Error (ME), Mean Percentage Error (MPE), Schwarz Bayesian Information Criteria (SBIC), Schwarz Bayesian Criteria (SBC), values and high R2. It was used for forecasting the GDP of Pakistan for the next 55 years from 2021-to 2075. Data were analyzed by using SPSS-21, Eviews-3, and Statgraphics-16. We have found that the best model is the Linear trend model. Based on this selected model, we have found that the GDP of Pakistan would become 2.51199 in 2035 and would become less in 2075 as compared to 2025.


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