Seasonality often accounts for the major part of quarterly or monthly movements in detrended macro-economic time series. In addition, business cycle nonlinearity is a prominent feature of many such series too. A forecaster can nowadays consider a wide variety of time series models which describe...

Nonlinear time series models have become fashionable tools to describe and forecast a variety of economic time series. A closer look at reported empirical studies, however, reveals that these models apparently fit well in-sample, but rarely show a substantial improvement in out-of-sample...

Using a standard 4-variable linear vector error correction model (VECM), we first show that the null hypothesis of linearity can be strongly rejected against the alternative of smooth transition autoregressive nonlinearity. An important result from this stage of the analysis is that the...

In this paper we put forward a new time series model, which describes nonlinearity and seasonality simultaneously. We discuss its representation, estimation of the parameters and inference. This seasonal STAR (SEASTAR) model is examined for its practical usefulness by applying it to 18 quarterly...

This paper surveys recent developments related to the smooth transition autoregressive [STAR] time series model and several of its variants. We put emphasis on new methods for testing for STAR nonlinearity, model evaluation, and forecasting. Several useful extensions of the basic STAR model,...

Forecasts of key macroeconomic variables may lead to policy changes of governments, central banks and other economic agents. Policy changes in turn lead to structural changes in macroeconomic time series models. To describe this phenomenon we introduce a logistic smooth transition autoregressive...

This article proposes a modified method for the construction of diffusion indexes in macroeconomic forecasting using principal component regres- sion. The method aims to maximize the amount of variance of the origi- nal predictor variables retained by the diffusion indexes, by matching the data...

We propose a new approach to deal with structural breaks in time series models. The key contribution is an alternative dynamic stochastic specification for the model parameters which describes potential breaks. After a break new parameter values are generated from a so-called baseline prior...

This paper presents the results of a survey held amongst all editorial board members of six journals. These journals in part focus on the development of models and methods for forecasting. The key question was whether one believes that the forecasting discipline has made progress in the last...

In business and in macroeconomics it is common practice to use econo- metric models to generate forecasts. These models can take any degree of sophistication. Sometimes it is felt by an expert that the model-based fore- cast needs adjustment. This paper makes a plea for a formal approach to such...