A REPARAMETRISED AUTOREGRESSIVE MODEL
FOR MODELLING GROSS DOMESTIC PRODUCT

dc.creator	Adedotun, Adedayo F., Taiwo, Abass, Olatayo, T. O.
dc.date	2020
dc.date.accessioned	2025-04-15T11:13:09Z
dc.description	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.
dc.format	application/pdf
dc.identifier	http://eprints.covenantuniversity.edu.ng/17549/
dc.identifier.uri	https://repository.covenantuniversity.edu.ng/handle/123456789/48271
dc.language	en
dc.subject	QA Mathematics
dc.title	A REPARAMETRISED AUTOREGRESSIVE MODEL FOR MODELLING GROSS DOMESTIC PRODUCT
dc.type	Article

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