A REPARAMETRISED AUTOREGRESSIVE MODEL FOR MODELLING GROSS DOMESTIC PRODUCT

Adedotun, Adedayo F. and Taiwo, Abass and Olatayo, T. O. (2020) A REPARAMETRISED AUTOREGRESSIVE MODEL FOR MODELLING GROSS DOMESTIC PRODUCT. ANNALS of Faculty Engineering Hunedoara – International Journal of Engineering. pp. 183-188. ISSN 1584 - 2665 (printed version); ISSN 2601 - 2332 (online); ISSN-L 1584 - 2665

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Abstract

This paper is used to propose a reparametrised autoregressive model that is capable of analyzing time series data that follows a non-Gaussian marginal distribution. The Anderson Darling Statistics was used to identify that Nigerian Gross domestic product followed a Gamma distribution. The proposed Gamma autoregressive (GAR) and classical autoregressive models were fitted using a Maximum Likelihood Estimation (MLE) method. The Akaike Information Criteria (AIC) was used to select AR(2) and GAR(2) as the optimal models but GAR(2) was chosen because it has the least value of AIC. The comparison between AR(2) and GAR(2) models based on the values of Mean absolute error (MAE), Mean absolute prediction error (MAPE) and Root mean square error (RMSE) indicted that GAR(2) will yield a more accurate forecast than AR(2). In essence, GAR model is a viable alternative and better model for analyzing GDP growth rate.

Item Type:	Article
Uncontrolled Keywords:	Non-Gaussian model, Classical autoregressive, Economic growth, reparameterization
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Engineering, Science and Mathematics > School of Mathematics
Depositing User:	nwokealisi
Date Deposited:	10 Nov 2023 09:10
Last Modified:	10 Nov 2023 09:10
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/17549

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