Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.10, No.23, 2019 13 Forecasting GDP Growth Rates of Bangladesh: An Empirical Study Liton Chandra Voumik *1 Md. Maznur Rahman 2 Md. Shaddam Hossain 1 Mahbubur Rahman 1 1. Lecturer, Department of Economics, Noakhali Science and Technology University, Bangladesh 2. Assistant Professor, Department of Economics, Noakhali Science and Technology University, Bangladesh 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 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. GDP is often considered the best measure of how well the economy is performing. The issues of GDP growth have become the most concerning amongst modern macroeconomic variables. 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). Since Adam Smith, economic growth has been an important topic in economic indicators and an important component of mainstream economics. The assessment of the current state of the economy is an important element in macroeconomic forecasting for a long- term analysis. Economic researchers are particularly interested in GDP forecasts for assessing and predicting the functional status of the economy of developing countries. Forecasting future economic outcomes is a vital component of the decision-making process for central banks, financial authorities, and economists for all countries. 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 ARIMA (Auto-Regressive-Integrated-Moving-Average) methodology. Box and Jenkins’ methodology has been used extensively by many researchers to highlight the future GDP growth. The market-based economy of Bangladesh is the 44 th largest in the world in nominal terms. 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. Literature Review A large variety of linear and nonlinear models are now available for modeling and forecasting macroeconomic time series data—see, for example, Terasvirta (2005), West (2005), Artis and Marcellino (2001), and White (2005) for recent overviews. Wei et al. (2010) applied data from China’s Shaanxi GDP from 1952 to 2007 to forecast the country’s GDP for the succeeding 6 years. Maity and Chatterjee (2012) scrutinized the forecasting of GDP growth for India applying an ARIMA (1,2,2) model. The results of their study displayed that predicted values follow a growing trend for the succeeding years. Zhao Ying used an ARIMA model with time series data of actual GDP from 1954 to 2004 in China to analyze and predict the national GDP growth pattern. Lu (2009) attempted to construct a time series model that was utilized to forecast the gross domestic product of China up to the first quarter of 2009. This paper was based on figures collected from secondary sources from the years1962 to 2008, Lu got