Macroeconomic time series such as total unemployment or total industrial production concern data which are aggregated across regions, sectors, or age categories. In this paper we examine whether forecasts for these aggregates can be improved by considering panel models for the disaggregate series. As many macroeconomic variables have nonlinear properties, we specifically focus on panels of nonlinear time series. We discuss the representation of such models, parameter estimation and a method for generating forecasts. We illustrate the usefulness of our approach for simulated data and for the US coincident index, making use of state-specific component series.

Business cycle, Data aggregation, Forecasting, Multi-level models, Nonlinearity, Panel of time series
dx.doi.org/10.1016/j.ijforecast.2005.04.015, hdl.handle.net/1765/11134
International Journal of Forecasting
Erasmus Research Institute of Management

Fok, D, van Dijk, D.J.C, & Franses, Ph.H.B.F. (2005). Forecasting aggregates using panels of nonlinear time series. International Journal of Forecasting, 21(4), 785–794. doi:10.1016/j.ijforecast.2005.04.015