# 12-042/4 (2012-04-20)

Falk Brauning, VU University Amsterdam; Siem Jan Koopman, VU University Amsterdam
Kalman filter, Mixed frequency; Nowcasting, Principal components, State space model, Unobserved Components Time Series Model
JEL codes:
C33, C53, E17

This discussion paper resulted in an article in the International Journal of Forecasting (2014). Volume 30, pages 572-584.

We explore a new approach to the forecasting of macroeconomic variables based on a dynamic factor state space analysis. Key economic variables are modeled jointly with principal components from a large time series panel of macroeconomic indicators using a multivariate unobserved components time series model. When the key economic variables are observed at a low frequency and the panel of macroeconomic variables is at a high frequency, we can use our approach for both nowcasting and forecasting purposes. Given a dynamic factor model as the data generation process, we provide Monte Carlo evidence for the finite-sample justification of our parsimonious and feasible approach. We also provide empirical evidence for a U.S. macroeconomic dataset. The unbalanced panel contain quarterly and monthly variables. The forecasting accuracy is measured against a set of benchmark models. We conclude that our dynamic factor state space analysis can lead to higher forecasting precisions when panel size and time series dimensions are moderate.