Many current seasonally adjusted level data are based on Census-X-11-type moving average filters applied to past and forecasted log-transformed observations, which is usually called the Census-X-11 ARIMA method. The forecasts are often generated from seasonal ARIMA models for the log-transformed...

In this paper we extend the univariate periodic integration model to multivariate cointegrated time series. We analyze representation issues of a multivariate periodic model. We argue that simple adding an index s to the parameters in an otherwise nonperiodic Vector AutoRegression (VAR) leads to...

In this paper we propose to consider a measure of the persistence of shocks in linear combinations of nonlinear processes, in order to investigate the possible presence of common long-run properties. We argue that such common persistence for nonlinear time series corresponds to the concept of...

This paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption series.

We address the issue of time varying persistence of shocks to macroeconomic time series variables by proposing a new and parsimonious time series model. Our model assumes that this time varying persistence depends on a linear combination of lagged explanatory variables, where this combination...