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 if 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 to generate 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, multi-level models, nonlinearity, panel of time series
Models with Panel Data (jel C23), Model Construction and Estimation (jel C51), Forecasting and Other Model Applications (jel C53), Business Fluctuations; Cycles (jel E32)
Econometric Institute Research Papers
Report / Econometric Institute, Erasmus University Rotterdam
Erasmus School of Economics

Fok, D, van Dijk, D.J.C, & Franses, Ph.H.B.F. (2004). Forecasting aggregates using panels of nonlinear time series (No. EI 2004-44). Report / Econometric Institute, Erasmus University Rotterdam. Retrieved from