TITLE:
Modeling GDP Using Autoregressive Integrated Moving Average (ARIMA) Model: A Systematic Review
AUTHORS:
Benard Muma, Austin Karoki
KEYWORDS:
Autoregressive Integrated Moving Average Model, Modeling GDP
JOURNAL NAME:
Open Access Library Journal,
Vol.9 No.4,
April
12,
2022
ABSTRACT: This paper examines the application of the Autoregressive Integrated Moving Average (ARIMA) Model in modeling GDP. The paper critically reviews the empirical literature on the application of ARIMA models in modeling GDP in various economies with the objective of establishing the appropriateness and popularity of ARIMA model in studying GDP. The paper concludes that ARIMA (2, 2, 2) and ARIMA (3, 1, 1) are significantly applied in studies involving 182-days treasury bills and ARIMA (1, 1, 0) 91-days treasury bills respectively. This paper proposes the application of Q-statistic for autocorrelation, Jarque-Bera (JB) test for normality and Box-Jenkins analysis for model identification and estimation, diagnosis as well as forecasting. The paper further presents that the correlogram of real GDP rate series can be used for GDP modeling, specifically recommending the application of ARCH and GARCH family models in studies involving the analysis of treasury bills. Lastly, this paper recommends the adoption of the expenditure approach to measure GDP and proposes a model that can be adopted in GDP modeling.