In this paper we put forward a new time series model, which describes nonlinearity and seasonality simultaneously. We …-transition nonlinearity and for time-varying seasonality. We find that the model fits the data well for 14 of the 18 series. We also consider … out-of-sample forecasting where we compare forecasts from the SEASTAR models with forecasts from nested models. It turns …

we examine the forecasting performance of various models for seasonality and nonlinearity using quarterly industrial …. In addition, business cycle nonlinearity is a prominent feature of many such series too. A forecaster can nowadays … production series for 17 OECD countries. We find that forecasting performance varies widely across series, across forecast …

found to perform better in out-of-sample forecasting than a benchmark linear model. An empirical illustration for US GDP …

contributed significantly to time series and financial econometrics, including forecasting co-volatilities via factor models with … latent factors, endogeneity and nonlinearity, sign-based portmanteau test for ARCH-type models with heavy-tailed innovations …

convenient framework for simultaneously handling nonlinearity, structural change and outliers in time series. Many popular …

Outliers and nonlinearity may easily be mistaken. This paper uses Monte Carlo methods to examine and compare the …

nonlinearity across economic variables. The model is a multivariate censored latent effects autoregression. The key feature of this … model is that nonlinearity appears as separate innovation-like variables. Common nonlinearity can then be easily defined as … not the other way around, and also that there is no common nonlinearity across the unemployment variables. …