Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models

Abstract

This paper analyses the effects of Gross Domestic Product growth (GDP) and Inflation rate (INF) on Unemployment rate (UMP) in Ghana's economy using covariance matrix and multiple regression models. The two models were examined separately on the same data of three variables and the different outputs analysed to determine the effectiveness among the two models. The analyses of the outputs highlight the significance of both predictor variables on unemployment rate in Ghana. Scatterplot and normal probability distribution (pnorm) graphs were used to analyse the normality of the predictor variables. Data on inflation rate and GDP growth spanning from 1991 to 2017 was used. The data was transformed to n X m matrix form for covariance-variance matrix analysis. The rows in the n by m data matrix were the multivariate observations on n units. Multiple regression analysis was performed on the data. Both the two methods provided the long-run effects of the two predictor variables on the unemployment rate. However, while multiple regression model could quantify the effect of each predictor variable on the predicted variable, the covariance matrix model only quantifies the relation existing between predictor variables and the predicted variable.