Transferfunktionsmodeller modellering och prognoser av Sjötransportindex
We have by Statistics Sweden (SCB) been given the task of using different dynamic regression models in order to forecast service price index for sea transport. The aim is to see whether these models provide better forecasts than those previously used. This essay aim to identify, estimate and evaluate the selected prediction models. Through our data material we were given access to 28 sightings of sea transport index during the period of 2004 q1 to 2010 q4. We have chosen to evaluate three different transfer function models, one ARIMA model and one naive forecasting model. The input variables we decided to test in our transfer function models were the price of petroleum products, the port activity in Swedish ports and the lending rate of Swedish Central bank. The results of our study suggest that transfer function models generally provide better models than the ARIMA model and the naive forecast model. Results also show that both the transfer function models and ARIMA model seem to provide better models than the naïve forecasting model. The transfer function model that gave the lowest forecasting errors had interest rate as an input variable.