This paper concerns estimating parameters in a high-dimensional dynamic factor model by the method of maximum likelihood. To accommodate missing data in the analysis, we propose a new model representation for the dynamic factor model. It allows the Kalman filter and related smoothing methods to evaluate the likelihood function and to produce optimal factor estimates in a computationally efficient way when missing data is present. The implementation details of our methods for signal extraction and maximum likelihood estimation are discussed. The computational gains of the new devices are presented based on simulated data sets with varying numbers of missing entries.

High-dimensional vector series, Kalman filtering and smoothing, Unbalanced panels of time series,
Journal of Economic Dynamics and Control
Erasmus Research Institute of Management

Jungbacker, B, Koopman, S.J, & van der Wel, M. (2011). Maximum likelihood estimation for dynamic factor models with missing data. Journal of Economic Dynamics and Control, 35(8), 1358–1368. doi:10.1016/j.jedc.2011.03.009