CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

Abstract

We introduce a bivariate bidimensional mixed‐effects regression model, motivated by the analysis of CO2$$ {\mathrm{CO}}_2 $$ emission levels and growth on OECD countries from 1990 to 2018. The model is able to capture heterogeneity across countries and allows for a full association structure among outcomes, assuming a discrete distribution for the random terms with a possibly different number of support points in each univariate profile. We test the behavior of the proposed approach via a simulation study, considering several factors such as the number of observed units, times, and levels of heterogeneity in the data. Empirically, we define an extended version of the STIRPAT model where all model parameters, and not only the mean, vary according to a regression model. Our empirical findings provide evidence of heterogeneous behaviors across countries and suggest the need of a flexible approach to properly reflect the heterogeneity in both the emission levels and the growth processes.

Modelling and Clustering Sea Conditions: Bivariate FiniteMixtures of Generalized Additive Models for Location, Shape, and Scale Applied to the Analysis of Meteorological Tides and Wave Heights

“…By employing mixture models, clusters are identified as mixtures of distributions, providing insights into the locations and shapes of clusters. Other examples are given in [24][25][26][27]; see [28] for a comprehensive review on mixture models. Parameter estimates are obtained by maximum likelihood, employing an expectation-maximization algorithm.…”