CO ₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

We introduce a bivariate bidimensional mixed-effects regression model, motivated by the analysis of CO 2 $$ {\mathrm{CO}}_2 emission levels and growth on OECD countries from 1990 to 2018. The model is able capture heterogeneity across allows for full association structure among outcomes, assuming discrete distribution random terms with possibly different number support points in each univariate profile. test behavior proposed approach via simulation study, considering several factors such as observed units, times, data. Empirically, we define an extended version STIRPAT where all parameters, not only mean, vary according model. Our empirical findings provide evidence heterogeneous behaviors suggest need flexible properly reflect both processes.