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- W3036191804 abstract "Economic time series often feature non-linear structures such as non-linear time trends, non-linear autoregressive effects, and non-linear interaction effects. In this paper, it is shown that artificial neural network regression models are suitable tools for the analysis of economic panel data because they allow for a compromise between the ability to model these features and the model size. As model specification is a concern in artificial neural network models, previous approaches are discussed critically. It is shown that the growth rates of the gross domestic product of 24 industrialized economies in the period 1992–2016 follow a non-linear time trend which cannot be explained by autoregressive features or polynomial time variables. The unrestricted functional form of the time trend in the artificial neural network model is also the main reason for the superior statistical performance compared to conventional panel models. This is confirmed by out-of-sample predictions for 2017." @default.
- W3036191804 created "2020-06-25" @default.
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- W3036191804 date "2020-09-01" @default.
- W3036191804 modified "2023-10-01" @default.
- W3036191804 title "Artificial neural network regression models in a panel setting: Predicting economic growth" @default.
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- W3036191804 doi "https://doi.org/10.1016/j.econmod.2020.06.008" @default.
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