Working Paper: CEPR ID: DP5620
Authors: George Kapetanios; Massimiliano Marcellino
Abstract: 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.
Keywords: Factor models; Principal components; Subspace algorithms
JEL Codes: C32; C51; E52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| growth rate of n (O40) | consistency of the factor space estimators (C51) |
| proposed methodology (C80) | improved estimation performance (C51) |
| proposed method (C59) | higher correlation between true and estimated common components (C10) |