Modelling and Forecasting the GDP of G7 Countries Using Arima Model

Abstract

The study investigated the empirical role of past values of G 7 countries GDP growth rates in its future realizations. Using the Box–Jenkins modelling method, the study utilized 250 in-sample quarterly time series data to forecast out-of-the-sample G7 countries GDP growth rates. The study sourced the GDP growth data from World Bank World Development Indicators (WDI) for the period between 2002 to 2022. The study results predict that G 7countries GDP will, on average, experience 4 percent quarterly growth rates for the coming three and half years. To solidify the validity of the forecasting results, the study conducted several ARIMA and rolling window diagnostic tests. The model errors proved to be white noise, the moving average (MA) and Autoregressive (AR) components are covariances stationary, and the rolling window test shows model stability within a 95% confidence interval. Purpose: To explore the effectiveness of ARIMA model in forecasting GDP of G 7 countries. Findings: The results indicate that   the GDP data of the G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States), the ARIMA (Autoregressive Integrated Moving Average) model proved effective in capturing the historical trends of GDP fluctuations. Practical Implications: These ARIMA-based forecasts can serve as useful tools for policymakers, allowing them to anticipate potential economic downturns and formulate appropriate monetary or fiscal policies. Financial institutions can integrate these GDP forecasts into their risk models to better manage credit risk, especially in countries with volatile GDP trends like the UK and Italy. Originality/value: The study provides a novel comparative perspective on GDP forecasting across multiple advanced economies, using ARIMA to identify country-specific economic trends. This multi-country analysis adds value to existing literature, which often focuses on individual countries.