Date: 2006
Type: Working Paper
A parametric estimation method for dynamic factor models of large dimensions
Working Paper, IGIER Working Paper, 2006/305
MARCELLINO, Massimiliano, KAPETANIOS, George, A parametric estimation method for dynamic factor models of large dimensions, IGIER Working Paper, 2006/305 - https://hdl.handle.net/1814/42365
Retrieved from Cadmus, EUI Research Repository
The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and discuss its theoretical properties. In particular, we show that it is possible to estimate consistently the factor space. We also develop a consistent information criterion for the determination of the number of factors to be included in the model. Finally, we conduct a set of simulation experiments that show that our approach compares well with existing alternatives.
Cadmus permanent link: https://hdl.handle.net/1814/42365
Series/Number: IGIER Working Paper; 2006/305
Files associated with this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |