Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We assess the power of diverse Artificial Neural-Network (ANN) models as forecasting tools for monthly inflation rates for 28 Organization for Economic Co-operation and Development (OECD) countries. In the context of short out-of-sample forecasting horizon we find that, on average, the ANN models were a superior predictor for inflation for 45% while the Autoregressive model of order one (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.