We examine recursive out-of-sample forecasting of monthly postwarU.S. core inflation and log price levels. We use theautoregressive fractionally integrated moving average model withexplanatory variables (ARFIMAX). Our analysis suggests asignificant explanatory power of leading indicators...

For many economic time-series variables that are observed regularly and frequently, for example weekly, the underlying activity is not distributed uniformly across the year. For the aim of predicting annual data, one may consider temporal aggregation into larger subannual units based on an...

In the paper we consider the role of seasonal intercepts in seasonal cointegration analysis. For the nonseasonal unit root, such intercepts can generate a stochastic trend with a drift common to all observations. For the seasonal unit roots, however, we show that unrestricted seasonal intercepts...

The popular "airline" model for a seasonal time series assumes that a variable needsdouble differencing, i.e. first and seasonal (or annual) differencing.The resultant time series can usually be described by a low order movingaverage model with estimated roots close to the unit circle. This...