Modelling and Forecasting of GDP in Bangladesh: An ARIMA Approach

Authors:

M. M. Miah,Mimma Tabassum,M. Shohel Rana,

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00012

Keywords:

GDP,ARIMA Modeling,Forecasting,Bangladesh,

Abstract

This paper aims to model and forecasting on GDP data of Bangladesh for the period of 1960 to 2017. To test the stationarity of the series graphical method, correlogram and unit root test were used. The time series plot of GDP shows a non-stationary pattern and overall this is like exponential curvature shape. Hence the data have been differenced twice to convert the data from non-stationary to stationary. From the autocorrelation function (ACF) and partial autocorrelation function (PACF) we obtain the order of the time series model. The chosen model was autoregressive integrated moving average ARIMA (1, 2, 1). The model has been fitted on data to estimate the parameters of autoregressive and moving average components of ARIMA (1, 2, 1) model. For residual diagnostics, correlogram, Q-statistic, histogram, and normality test were used. Also, Chow test was used for stability testing. Using model selection criterion and checking model adequacy, wesee that the model is suitable in shape. It is found that the forecast values of GDP in Bangladesh are steadily improving over the next thirteen years.

Refference:

I.Box GEP, Gwilym MJ, Gregory CR. Time Series Analysis: Time Series Analysis Forecasting & Control. New Jersey: Prentice Hall, Englewood Cliffs; 1994.

II.Dickey DA, Fuller WA. Distributions of the Estimators for Autoregressive Time Series with a Unit Root. J Am Stat Assoc. 1979; 74(366),pp:427–481.

III.Dr. ChaidoDritsaki (2015). Forecasting Real GDP rate through Econometric Models: An Empirical Study from Greece. J of Internal Business and Economics, 3(1), pp: 13-19.

IV.Gujarati DN, Porter DC, Gunasekar S. Econometric Modeling: Specification and Diagnostic Testing. Basic Econometrics. 4th Edn. McGraw Hill International; 2003.

V.Hanke JE, Wichern DW. Business Forecasting. 8th Edn. Int J Forecast. 2005; 22(4), pp: 823–824.

VI.Imon AHMR. Box-Jenkins ARIMA Models: Introduction to Regression TimeSeries and Forecasting. NanitaProkash; 2017.VII.Jovanovic, B. &Petrovska M. (2010). Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting. Working Paper, National Bank of the Republic of Macedonia.

VIII.Ljung, G. M., & Box G. E. P. (1978). On a measure of a lack of fit in time series models. Biometrika, 75(2), pp: 335-346.

IX.Maity, B., &ChatterjeeB. (2012). Forecasting GDP growth rates of India: An empirical study. IntJof Economics and Management Sciences, 1(9), pp: 52-58.

X.Ning, W., Kuan-jiang, B. and Zhi-fa, Y. (2010), Analysis and forecast of Shaanxi GDP based on the ARIMA model, Asian Agricultural Research, Vol.2 No. 1, pp. 34-41.

XI.Shahini, L. &Haderi S. (2013). Short term Albanian GDP forecast: One quarter to one year ahead. European Scientific Journal, 9(34),pp: 198-208.

XII.Wei Ning, BianKuan-Jiang. &Yuan Zhi-fa (2010).Analysis and forecast of Shaanxi GDP based on the ARIMA model. Asian Agricultural Research, 2(1), pp: 34-41.

XIII.Zakai, M. (2014). A time series modeling on GDP of Pakistan. J of Contemporary Issues in Business Research,3(4), pp: 200-210.

XIV.Zhang, H. (2013). Modeling and forecasting regional GDP in Sweden using autoregressive models. Working Paper, HögskolanDalarna University, Sweden.

M. M. Miah, Mimma Tabassum, M. Shohel Rana View Download