Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87470
Authors: 
Brauning, Falk
Koopman, Siem Jan
Year of Publication: 
2012
Series/Report no.: 
Tinbergen Institute Discussion Paper 12-042/4
Abstract: 
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.
Subjects: 
Kalman filter
Mixed frequency
Nowcasting
Principal components
State space model
Unobserved Components Time Series Model
JEL: 
C33
C53
E17
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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