A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models

Catherine Doz (1, 2) , Domenico Giannone (3, 4) , Lucrezia Reichlin (4, 5)

1 CES - Centre d'économie de la Sorbonne
2 PSE - Paris School of Economics
3 ECARES - European Center for Advanced Research in Economics and Statistics
4 CEPR - Center for Economic Policy Research
5 London Business School

Is maximum likelihood suitable for factor models in large cross-sections of time series? We answer this question from both an asymptotic and an empirical perspective. We show that estimates of the common factors based on maximum likelihood are consistent for the size of the cross-section (n) and the sample size (T) going to in¯nity along any path and that maximum likelihood is viable for n large. The estimator is robust to misspecification of cross-sectional and time series correlation of the idiosyncratic components. In practice, the estimator can be easily implemented using the Kalman smoother and the EM algorithm as in traditional factor analysis.

Mots clés: Time Series; Misspecification; Factor Analysis

Domaines: Méthodes et statistiques; Economies et finances

Catherine Doz, Domenico Giannone, Lucrezia Reichlin. A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models. Review of Economics and Statistics, 2012, 94 (4), pp.1014-1024. ⟨10.1162/REST_a_00225⟩. ⟨hal-00638440⟩