Comparison between multiple regression and multivariate adaptive regression splines for predicting CO2 emissions in ASEAN countries.

Global warming due to the rapid increase in greenhouse gas emissions, mainly carbon dioxide (CO2), is a worldwide issue that leads to escalating pollutions and emerging diseases. The comparative performances of multiple regression (MR) and multivariate adaptive regression splines (MARS) for statistical modelling of CO2 emissions are analyzed in ASEAN countries over the period of 1980-2007. The regression models are fitted individually for every potential variable investigated so as to find the best-fit parametric or non-parametric model. The results show a significant difference between the performance of MR and MARS models with the inclusion of interaction terms. The MARS model is computationally feasible and has better predictive ability than the MR model in predicting CO2 emissions. In overall, MARS can be viewed as a modification of stepwise regression that enhances the latter's performance in the regression setting.