Working Paper: CEPR ID: DP2509
Authors: Mario Forni; Marco Lippi
Abstract: This paper, along with the companion paper Forni, Hallin, Lippi and Reichlin (1999), introduces a new model-the generalized dynamic factor model-for the empirical analysis of financial and macroeconomic data sets characterized by a large number of observations both cross-section and over time. This model provides a generalization of the static approximate factor model of Chamberlain (1983) and Chamberlain and Rothschild (1983) by allowing serial correlation within and across individual processes, and of the dynamic factor model of Sargent and Sims (1977) and Geweke (1977) by allowing for non-orthogonal idiosyncratic terms. While the companion paper concentrates on identification and estimation, here we give a full characterization of the generalized dynamic factor model in terms of observable spectral density matrices, thus laying a firm basis for empirical implementation of the model. Moreover, the common factors are obtained as limits of linear combinations of dynamic principal components. Thus the paper reconciles two seemingly unrelated statistical constructions.
Keywords: dynamic factor structure; dynamic principal components; idiosyncratic components; large cross-sections
JEL Codes: C13; C33; C43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| GDFM (E17) | dynamic principal components (C38) |
| dynamic principal components (C38) | GDFM (E17) |
| GDFM (E17) | common factors and idiosyncratic components (C38) |
| relaxing orthogonality assumption (C20) | contemporaneous and lagged correlations (C32) |
| contemporaneous and lagged correlations (C32) | dynamics of the model (C69) |