Periodic models for seasonal data allow the parameters of the model to vary across the different seasons. This paper uses the components of UK consumption to see whether the periodic autoregressive (PAR) model yields more accurate forecasts than non-periodic models, such as the airline model of...

In economics density forecasts are rarely available, and as a result attention has traditionally focused on poit forecasts of the mean and the use of mean square error statistics to represent the loss function. We extend the methods of forecasts density evaluation in Diebold, Gunther and Tay...

We consider the forecasting performance of two SETAR exchange rate models proposed by Krager and Kugler (1993). Assuming that the models are good approximations to the data generating process, we show that whether the non-linearities inherent in the datacan be exploited to forecast better than a...

We propose testing for business cycle asymmetries in Markov-switching autoregressive (MS-AR) models. We derive the parametric restrictions on MS-AR models that rule out types of asymmetries such as deepness, steepness, and sharpness, and set out a testing procedure based on Wald statistics which...

While there has been a great deal of interest in the modeling of non-linearities in economic time series, there is no clear consensus regarding the forecasting abilities of non-linear time series models. We evaluate the performance of two leading non-linear models in forecasting post-war US GNP,...

The behavior of difference-stationary and trend-stationary processes has been the subject of considerable analysis in the literature. Nevertheless, there do not seem to be any direct comparisons of properties of each for both being potential data-generation processes. We look at the consequences...