Modelling Multiple Regimes in Economic Growth by Mixtures of Generalised Nonlinear Models

Sanela Omerovic*, Herwig Friedl, Bettina Grün

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)124-135
Number of pages12
JournalEconometrics and Statistics
Publication statusPublished - Apr 2022


  • EM algorithm
  • Finite mixture model
  • Generalised nonlinear model
  • Solow model

ASJC Scopus subject areas

  • Economics and Econometrics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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