Missing Observations in Dynamic Econometric Models: A Partial Synthesis

Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 25)


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.


Likelihood Function Kalman Filter State Space Model State Space Form Missing Observation 
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Copyright information

© Springer-Verlag Berlin Heidelberg 1984

Authors and Affiliations

  1. 1.London School of EconomicsUK
  2. 2.Australian National UniversityAustralia

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