Sectoral Contribution to Nigeria’s Gross Domestic Product (GDP) Growth Rate: A Study of Multicollinearity in Aggregated Time Series Data

Abstract

The study examined aggregated GDP time series data for the period 1991 – 2010 to analyze the contributions of each sector to Nigeria’s GDP growth; and observe deficiencies inherent in the use of Ordinary Least Square (OLS) technique in GDP studies. Secondary data was used for the study. The data was sourced from the Central bank of Nigeria (CBN), and the National Bureau of Statistics (NBS). Analysis was carried out using graphs, tables, OLS regression technique, pairwise correlation, and Generalized Ridge Regression Analysis (GRR). Findings show an increasing trend in aggregate GDP for the study period, with an average GDP growth rate of 4.98%. Severe multicolinearity was detected in the GDP time series data using OLS regression with poor relationships, and high R2 value but few significant variables. The pairwise correlation coefficients also showed the presence of moderate to severe multicollinearity in the aggregate GDP variables. As a remedial measure, Generalized Ridge Regression analysis was employed. The GRR result showed a more well behaved model compared to the OLS implying a more precise estimation of the regression coefficients for the GRR model, which show a better influence of each sector’s contribution to the GDP growth rate within the twenty-year period. Therefore, it would be more appropriate for researchers in GDP time series studies to account for the problem of multicollinearity by utilizing Ridge regression technique for estimation since all Ridge regression models give more robust estimates than the OLS models.