Naı̈ve, ARIMA, nonparametric, transfer function and VAR models: A comparison of forecasting performance

We examine the forecasting performance of a number of parametric and nonparametric models based on a training–validation sample approach and the use of rolling short-term forecasts to compute root mean-squared errors. We find that the performance of these models is better than that of the naı̈ve, no-change model. The use of bivariate models (like VAR and transfer functions) provides additional root mean-squared error reductions. In many cases...