Autoregressive Integrated Moving Average Predictive Modelling for Gross Domestic Product of China, Pakistan, and Bangladesh

Gross Domestic Product (GDP) of a nation is an important index which reflects health and performance of an economy and its aggregate income. In this paper, annual GDP data of three Asian economies for the time period 1960 – 2022 is used for predictive Autoregressive Integrated Moving Average (ARIMA) modelling. ARIMA is a time series analysis method that can capture temporal tendencies and trends in the data series. We seek to gain insights into the future expected trajectory of economic growth in the selected countries through long-term predictions for the time period 2023 – 2037. Augmented Dick Fuller (ADF) test is used to asses stationarity of the data. In the present empirical study, stationarity at the second order differencing with ARIMA (0, 2, 2) model is identified to predict GDP of China, ARIMA (2, 2, 1) model is identified to predict GDP of Pakistan, and ARIMA (0, 2, 1) model is identified to predict GDP of Bangladesh for the next 15 years.  The finding shows that the forecast values of China’s GDP will be $14123.90 per capita in 2023 and $29842.64  per capita in 2037 Pakistan’s GDP will be $1589.066  per capita in 2023 and $2115.446  per capita in 2037, and Bangladesh’s GDP will be $2880.167  per capita in 2023 and $5566.303  per capita in 2037, Our study provides skeletal guidance for governmental bodies and direct  investors who rely for business  planning and strategizing of the resources on reliable predictions of GDP per capita. Advance knowledge about futuristic GDP level enables administrators, investors and policymakers to make informed economic decisions that may steer economic growth, stability and development in an optimum direction

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.