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

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  • Omerovic, Sanela
  • Friedl, Herwig
  • Grün, Bettina

Abstract

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.

Suggested Citation

  • Omerovic, Sanela & Friedl, Herwig & Grün, Bettina, 2022. "Modelling Multiple Regimes in Economic Growth by Mixtures of Generalised Nonlinear Models," Econometrics and Statistics, Elsevier, vol. 22(C), pages 124-135.
    • Handle: RePEc:eee:ecosta:v:22:y:2022:i:c:p:124-135
    DOI: 10.1016/j.ecosta.2021.02.008
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    1. Jiacheng Xue & Weixin Yao & Sijia Xiang, 2025. "Machine learning embedded EM algorithms for semiparametric mixture regression models," Computational Statistics, Springer, vol. 40(1), pages 205-224, January.

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