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Artificial neural network regression models: Predicting GDP growth

Malte Jahn

No 185, HWWI Research Papers from Hamburg Institute of International Economics (HWWI)

Abstract: Artificial neural networks have become increasingly popular for statistical model fitting over the last years, mainly due to increasing computational power. In this paper, an introduction to the use of artificial neural network (ANN) regression models is given. The problem of predicting the GDP growth rate of 15 industrialized economies in the time period 1996-2016 serves as an example. It is shown that the ANN model is able to yield much more accurate predictions of GDP growth rates than a corresponding linear model. In particular, ANN models capture time trends very flexibly. This is relevant for forecasting, as demonstrated by out-of-sample predictions for 2017.

Keywords: neural network; forecasting; panel data (search for similar items in EconPapers)
JEL-codes: C45 C53 C61 O40 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
Date: 2018
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