Missing Observations in Dynamic Econometric Models: A Partial Synthesis
A number of methods for carrying out the maximum likelihood estimation of a dynamic econometric model with missing observations are examined. These include the approach suggested by Sargan and Drettakis and a method based on the EM algorithm. The link between the different methods is explored and it is argued that in all cases the necessary computations can be carried out most efficiently by putting the model in state space form and applying the Kalman filter.
KeywordsLikelihood Function Kalman Filter State Space Model State Space Form Missing Observation
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