Working Paper: CEPR ID: DP5724
Authors: Catherine Doz; Domenico Giannone; Lucrezia Reichlin
Abstract: This paper considers quasi-maximum likelihood estimations of a dynamic approximate factor model when the panel of time series is large. Maximum likelihood is analyzed under different sources of misspecification: omitted serial correlation of the observations and cross-sectional correlation of the idiosyncratic components. It is shown that the effects of misspecification on the estimation of the common factors is negligible for large sample size (T) and the cross-sectional dimension (n). The estimator is feasible when n is large and easily implementable using the Kalman smoother and the EM algorithm as in traditional factor analysis. Simulation results illustrate what are the empirical conditions in which we can expect improvement with respect to simple principle components considered by Bai (2003), Bai and Ng (2002), Forni, Hallin, Lippi, and Reichlin (2000, 2005b), Stock and Watson (2002a,b).
Keywords: factor model; large cross-sections; quasi maximum likelihood
JEL Codes: C32; C33; C51
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| size of the dataset (n and t) (C55) | accuracy of the estimated factors (C51) |
| cross-sectional dimension (n) (C21) | precision of the estimates (C13) |
| traditional factor analysis methods (C38) | consistent estimates (C51) |