Background/Objectives: This study aims to apply time series tools ARIMA and Exponential smoothing methods to model and forecast GDP growth rates in the economy of Bangladesh. Forecasting of GDP growth rate is an important topic in macroeconomics.
Methods/ Statistical analysis: The data was collected from World Development Indicators (WDI) and it has been collected over a period of 37 years by WDI, World Bank. We applied Phillips–Perron (PP) and Augmented Dickey–Fuller (ADF) tests to investigate the stationary character of the data. Stata and R statistical software was used to construct a class of Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing methods to model and forecast the GDP growth.
Findings: We applied several ARIMA (P, I, Q) models and applied the ARIMA (1,1,1) model as best for forecasting. This ARIMA (1,1,1) model was chosen based on the minimum values of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Also, we applied the Exponential Smoothing measurements to forecast the GDP growth rate. In addition, among all the Exponential Smoothing models, the triple exponential model better analyzed the data based on lowest Sum of Square Error (SSE) and Root Mean Square Error (RMSE). Using these models, the numeric figure of future GDP growths are forecasted. Statistical outcomes illustrate that Bangladesh’s GDP growth rate is an increasing trend that will continue rising in the future.
Improvements/Applications: This finding will help policymakers and academicians to formulate economic and business strategies more precisely.