Forecasting GDP Growth Rates of Bangladesh: An Empirical Study

Md. Maznur Rahman ,Liton Chandra Voumik ,Md. Shaddam Hossain ,Mahbubur Rahman

doi:10.7176/jesd/10-23-02

Md. Maznur Rahman , Liton Chandra Voumik + Show 2 more

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https://doi.org/10.7176/jesd/10-23-02

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Abstract

The Gross Domestic Product (GDP) is the market value of all goods and services produced within the boundary of a nation in a year. This paper aims to apply time series tools and forecast GDP growth in the Bangladesh economy. Forecasting of time series is an important topic in macroeconomics. We collected the data from World Development Indicators (WDI) and it has been collected over a period of 37 years by WDI, World Bank. Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were applied to investigate the stationary character of the data. Stata and R statistical software was used to build a class of Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing methods to model the GDP growth. We applied several ARIMA (P, I, Q) models and employed 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 to forecast the GDP growth rate. In addition, among 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 values of future GDP growth rates are forecasted. Statistical results show that Bangladesh’s GDP growth rate is an increasing trend that will continue rising in the future. This finding will help policymakers and academicians to formulate economic and business strategies more precisely . Keywords: Stationary time series, ARIMA, Time Series Forecasting, Exponential Smoothing, GDP growth rate, GDP growth in Bangladesh DOI : 10.7176/JESD/10-23-02 Publication date: December 31 st 2019

Highlights

Gross Domestic Product (GDP) is the aggregate statistic of all economic activity; it captures the broadest coverage of the economy better than any other macroeconomic variables
This paper aims to apply time series tools and forecast GDP growth in the Bangladesh economy
For the forecasting of time series, We used models that are based on a methodology that was first developed by Box and Jenkins (1976), known as Autoregressive Integrated Moving Average (ARIMA) (Auto-Regressive-Integrated-Moving-Average) methodology

Summary

Introduction

GDP is the aggregate statistic of all economic activity; it captures the broadest coverage of the economy better than any other macroeconomic variables. It is the market value of all final goods and services produced within the borders of a nation in a year. The growth is regarded as the most important index for assessing national economic development, economic health, and for judging the operating status of the macro economy (Ning et al 2010). Economic researchers are interested in GDP forecasts for assessing and predicting the functional status of the economy of developing countries. The steady increase of its economic growth means that Bangladesh, a less developed country, could be predicted to come out of its economic status quo. Given the new developments in Bangladesh’s GDP, economists are often inconclusive about how long the trend will continue

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