Inflation Forecasting in Sweden using Single Hidden Layer Feedforward Artificial Neural Networks
Ulf Sefastsson, Per Sefastsson · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2017
Inflation affects many economic processes, and it is therefor crucial for economic agents to have reliable forecasts of it. In this thesis, single hidden layer feedforward artificial neural networks were used to predict the year-on-year consumer price index inflation rate in Sweden for the period 2013-01-01 – 2016-06-30. Separate networks were estimated for each prediction horizon, ranging from 1 to 24 months. The root mean square errors were computed for each horizon, which were then compared with the predictions issued by the Riksbank and two linear models (Autoregressive Moving Average and Autore- gressive) for the same period. The results show that the networks outperform the Riksbank’s predictions on 1–5 and 11–24 months, the ARMA model on 1–5 and 9–24 months, and the AR model on 1, 3, 5 and 20 – 23 months. The main conclusion is that artificial neural networks do have potential in forecasting the Swedish consumer price index inflation rate. There are, how- ever, several limitations in this thesis that need to be addressed and potential improvements to be investigated before a clear verdict can be made.