This paper attempts to forecast the
economic performance of Bangladesh measured with annual GDP data using an
Autoregressive Integrated Moving Average (ARIMA) Model followed by test of
goodness of fit using AIC (Akaike Information Criterion) and BIC (Bayesian
Information Criterion) index value among six ARIMA models along with several
diagnostic tests such as plotting ACF (Autocorrelation Function), PACF (Partial
Autocorrelation Function) and performing Unit Root Test of the Residuals
estimated by the selected forecasting ARIMA model. We have found the
appropriate ARIMA (1,0,1) model useful in predicting the GDP growth of
Bangladesh for next couple of years adopting Box-Jenkins approach to construct
the ARIMA (p,r,q) model using the GDP data of Bangladesh provided in the World
Bank Data stream from 1961 to 2019.
JEL classification numbers: B22, B23, C53.
Keywords: GDP growth, ACF, PACF, Stationary, ARIMA (p,r,q) model, Forecasting.