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Predicting Economic Performance of Bangladesh using Autoregressive Integrated Moving Average (ARIMA) model

Author

Listed:
  • Raad Mozib Lalon
  • PhD
  • Nusrat Jahan

Abstract

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. Â JEL classification numbers: B22, B23, C53.

Suggested Citation

  • Raad Mozib Lalon & PhD & Nusrat Jahan, 2021. "Predicting Economic Performance of Bangladesh using Autoregressive Integrated Moving Average (ARIMA) model," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 11(2), pages 1-5.
    • Handle: RePEc:spt:apfiba:v:11:y:2021:i:2:f:11_2_5
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    More about this item

    Keywords

    GDP growth; ACF; PACF; Stationary; ARIMA (p; r; q) model; Forecasting.;
    All these keywords.

    JEL classification:

    • B22 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Macroeconomics
    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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