A Model Comparison to Forecast Gross Domestic Product (GDP) in China

Abstract

Gross Domestic Product (GDP) is an accurate indicator to measure the size of the economic performance of a country and its growth rate. This study focuses on finding a suitable model to forecast GDP in China, which is one of the world’s largest and most rapidly developing economies. A simple linear regression model with AR(1) error structure and Autoregressive Integrated Moving Average (ARIMA) model were developed and compared for the purpose. A secondary data set which includes GDP in China from 1952 to 2020 was used for this study and the sample size was 69. Residual diagnostics tests were conducted to check the assumptions and model adequacy of each model. It was found that out of the fitted models, ARIMA (1,1,1) is the most appropriate model to forecast GDP in China as it gave lower MAE and RMSE compared to fitted simple linear regression model with AR(1) error structure. Model comparison was done using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The predicted values for 2023, 2024 and 2025 are 1436349, 1447149 and 1457950 respectively. E-views 8.0 and Minitab software were used to analyze the data.