We assess the power of diverse artificial neural-network models (ANN) as forecasting tools for monthly inflation rates for 28 OECD countries. In the context of short outof-sample forecasting horizon we find that, on average, the ANN models were a superior predictor for inflation for 45% while the AR1 model performed better for 23% of the countries. Furthermore, we develop arithmetic combinations of several ANN models and find that these may also serve as credible tools for forecasting inflation.JEL Classification: C51, C52, C53, E31, E37
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