VICTORIA OLAYINKA ADEKOYA

This research undertakes a comprehensive statistical analysis of Nigeria's Gross Domestic Product (GDP) spanning a decade, with a focus on estimating Autoregressive (AR) models using two prominent statistical methods: the Yule- Walker method and the Least Squares method. The study aims to provide statistical insights into the underlying dynamics of Nigeria's economic performance during this period. The research commences by delineating the statistical framework of AR models, which offer a statistical representation of a time series based on its past values. Subsequently, the Yule-Walker method is introduced, a statistical technique leveraging autocorrelation functions to estimate AR model parameters. The statistical properties of Yule-Walker estimators are elucidated in the context of Nigeria's GDP data. In contrast, the Least Squares method is presented as an alternative statistical
approach, characterized by its objective to minimize the sum of squared prediction errors. A statistical framework for the least squares estimators is outlined, providing insights into the statistical properties of parameter estimates and their significance in explaining variations in Nigeria's GDP. The core of the research involves the statistical analysis of Nigeria's GDP time series data over the 10-year period. Both the Yule-Walker and Least Squares methods are applied to estimate AR models tailored to the GDP data. The statistical comparison is based on goodness-of-fit statistics, such as the Akaike Information Criterion (AIC), to evaluate the models' adequacy in capturing the statistical patterns within the GDP dataset.