TY - JOUR
T1 - Maximum likelihood estimation for dynamic factor models with missing data
AU - Jungbacker, B.M.J.P.
AU - Koopman, S.J.
AU - van der Wel, M.
PY - 2011
Y1 - 2011
N2 - 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. © 2011 Elsevier B.V.
AB - 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. © 2011 Elsevier B.V.
U2 - 10.1016/j.jedc.2011.03.009
DO - 10.1016/j.jedc.2011.03.009
M3 - Article
SN - 0165-1889
VL - 35
SP - 1358
EP - 1368
JO - Journal of Economic Dynamics and Control
JF - Journal of Economic Dynamics and Control
IS - 8
ER -