Summary
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.
We are grateful to a number of participants in the Symposium for their comments. Particular thanks must go to Robert Shumway, Craig Ansley and Osvaldo Ferreiro. We are also grateful to Frank Dunston and Mark Watson for helpful comments on an earlier draft, particularly on the section dealing with the EM algorithm. We remain responsible for any errors. We would like to thank the SSRC for financial support in connection with the Programme in Methodology, Inference and Modelling in Econometrics at the London School of Economics.
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Harvey, A.C., McKenzie, C.R. (1984). Missing Observations in Dynamic Econometric Models: A Partial Synthesis. In: Parzen, E. (eds) Time Series Analysis of Irregularly Observed Data. Lecture Notes in Statistics, vol 25. Springer, New York, NY. https://doi.org/10.1007/978-1-4684-9403-7_6
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DOI: https://doi.org/10.1007/978-1-4684-9403-7_6
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