The new model class of mixtures of generalised nonlinear models (GNMs) is introduced. The model is specified, identifiability issues discussed, the fitting in a maximum likelihood framework using the expectation-maximisation (EM) algorithm outlined and an appropriate computational implementation introduced. The new model class is applied to capture cross-country heterogeneity when considering the augmented Solow model including human capital accumulation as underlying model structure. The inherent heterogeneity is attributed to multiple regimes being present within the selected country data set. The results highlight that country-specific differences lead to distinct components. Countries belonging to the same component exhibit convergence to a homogeneous steady state. The components differ in the initial technological endowment and the contribution of the economic variables to economic growth.

引入了广义非线性模型（GNM）混合的新模型类。指定模型，讨论可识别性问题，使用概述的期望最大化（EM）算法在最大似然框架中进行拟合，并引入适当的计算实现。当考虑包括人力资本积累作为基础模型结构的增强索洛模型时，新模型类用于捕捉跨国异质性。固有的异质性归因于所选国家数据集中存在的多个制度。结果强调，特定国家的差异导致不同的组成部分。属于同一组成部分的国家表现出趋同于同质稳定状态。