Application Of A Modified G-Parameter Prior (g=1/n^5) In Bayesian Model Averaging To CO2 Emissions In Nigeria

Abstract

Bayesian Model Averaging (BMA) is a variable selection approach that takes model uncertainty into account by averaging over the weights by their posterior model probabilities. Of concern are the priors to be used for the quantities of interest and the model choice. Using uniform prior for the model choice as supported by the literature, this study elicits a modified g- parameter prior in BMA in a normal linear regression models to model CO₂ emissions in Nigeria.....

Keywords: Variable Selection, Industrial Sector, Posterior Inclusion Probabilities.