Forecasting the unemployment rate when the forecast loss function is asymmetric

This paper studies forecasts when the forecast loss function is asymmetric, using Australian unemployment rates as an example. We focus on simple univariate models including autoregressive models and self-exciting threshold autoregressive models and we employ the same asymmetric quadratic loss function that is used for forecast evaluation at the model selection, model estimation and forecast combination stages. In particular, to incorporate the asymmetric loss function into model selection, we suggest the use of crossvalidation associated with the estimation methods that also incorporate the same loss function. Our forecasting results show considerable improvement when the same loss function is used for both estimation, combination and evaluation. However we do not observe gains from using the asymmetric loss function to select forecasting models compared with the conventional model selection methods based on squared-error loss. Forecast combination results show that using combination methods that account for asymmetric loss can attenuate the disadvantages of ignoring asymmetric loss when producing individual forecasts.