A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions
U of London Queen Mary Economics Working Paper No. 489
53 Pages Posted: 12 May 2003
Date Written: March 2003
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 methodology for estimating factors from large datasets based on state space models, discuss its theoretical properties and compare its performance with that of two alternative estimation approaches based, respectively, on static and dynamic principal components. The new method appears to perform best in recovering the factors in a set of simulation experiments, with static principal components a close second best. Dynamic principal components appear to yield the best fit, but sometimes there are leakages across the common and idiosyncratic components of the series. A similar pattern emerges in an empirical application with a large dataset of US macroeconomic time series.
JEL Classification: C32, C51, E52
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting
By Mario Forni, Marc Hallin, ...
-
Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
-
By James H. Stock and Mark W. Watson
-
Monetary Policy in a Data-Rich Environment
By Ben S. Bernanke and Jean Boivin
-
Eurocoin: A Real Time Coincident Indicator of the Euro Area Business Cycle
By Filippo Altissimo, Antonio Bassanetti, ...
-
Are More Data Always Better for Factor Analysis?
By Jean Boivin and Serena Ng
-
Implications of Dynamic Factor Models for VAR Analysis
By James H. Stock and Mark W. Watson
-
By Domenico Giannone, Lucrezia Reichlin, ...
-
By Domenico Giannone, Lucrezia Reichlin, ...