We study how the output gap affects potential output over time-i.e., the dynamic hysteresis effect. To do so, we introduce novel unobserved components (UC) models that consider hysteresis as a sequence of lagged effects, thus separating the long-run recession-induced adverse effects from other...

Structural time series models are formulated in terms of components, such as trends, seasonals and cycles, that have a direct interpretation. As well as providing a framework for time series decomposition by signal extraction, they can be used for forecasting and for ‘nowcasting’. The...

We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for...

We introduce a high-dimensional structural time series model, where co-movement between the components is due to common factors. A two-step estimation strategy is presented, which is based on principal components in differences in a first step and state space methods in a second step. The...

Risk is at the center of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments in planning counter- measures, the risk and severity of counterparty default concerns bank risk managers on a daily basis and the risk of...

We propose a multivariate simultaneous unobserved components framework to determine the two-sided interactions between structural trend and cycle innovations. We relax the standard assumption in unobserved components models that trends are only driven by permanent shocks and cycles are only...

Recent releases of X-13ARIMA-SEATS and JDemetra+ enable their users to choose between the non-parametric X-11 and the parametric ARIMA model-based approach to seasonal adjustment for any given time series without the necessity of switching between different software packages. To ease the...