Missing Observations in Dynamic Econometric Models: A Partial Synthesis

  • A. C. Harvey
  • C. R. McKenzie
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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  1. Anderson, B.D.O. and J.B. Moore (1979), Optimal Filtering, Prentice-Hall, Englewood Cliffs.MATHGoogle Scholar
  2. Dempster, A. P., N.M. Laird and D. B. Rubin (1977), “Maximum Likelihood from Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society (B), 39, 1–38.MathSciNetMATHGoogle Scholar
  3. Drettakis, E.G. (1973), “Missing Data in Econometric Estimation,” Review of Economic Studies, 40, 537–552.MATHCrossRefGoogle Scholar
  4. Engle, R.F. and M. Watson (1981), “A One Factor Multivariate Time Series Model of Metropolitan Wage Rates,” Journal of the American Statistical Association, 76, 774–781.Google Scholar
  5. Engle, R.F. and M. Watson (1982), “The EM Algorithm for Dynamic Factor and MIMIC,” H.I.E.R. Discussion Paper, Harvard University.Google Scholar
  6. Hartley, H.O. (1958), “Maximum Likelihood Estimation from Incomplete Data,” Biometrics, 14, 174–194.MATHCrossRefGoogle Scholar
  7. Harvey, A.C. (1981), Time Series Models, Phillip Allan, Deddington, and John Wiley/Halstead Press, New York.MATHGoogle Scholar
  8. Harvey, A.C., C. R. McKenzie, D. Blake and M. Desai (1981), “Data Revisions in the UK,” paper presented at ASA-CENSUS-NBER Conference on Applied Time Series Analysis of Economic Data. To be published in the conference volume.Google Scholar
  9. Harvey, A.C. and C.R. McKenzie (1981), “Estimation of Systems of Equations when there is Contemporaneous Aggregation of Dependent Variables,” London School of Economics Econometrics Programme Discussion Paper No. A.30.Google Scholar
  10. Jazwinski, A.H. (1970), Stochastic Processes and Filtering Theory, Academic Press, New York.MATHGoogle Scholar
  11. Johnston, J. (1972), Econometric Methods, Second Edition, McGraw-Hill, New York.Google Scholar
  12. Kohn, R. and C. Ansley, “Fixed Interval Estimation in State Space Models When Some of the Data Are Missing or Aggregated,” forthcoming in Biometrika.Google Scholar
  13. Pagan, A. (1980), “Some Identification and Estimation Results for Regression Models with Stochastically Varying Parameters,” Journal of Econometrics, 13, 341–363.MathSciNetMATHCrossRefGoogle Scholar
  14. Rosenberg, B. (1973), “Random Coefficient Models: The Analysis of a Cross-Section of Time Series by Stochastically Convergent Parameter Regressions,” Annals of Economic and Social Measurement, 2, 399–428.Google Scholar
  15. Sargan, J.D. and Drettakis, E.G. (1974), “Missing Data in an Autoregressive Model,” International Economic Review, 15, 39–58.MathSciNetMATHCrossRefGoogle Scholar
  16. Schweppe, F. (1965), “Evaluation of Likelihood Functions for Gaussian Signals,” IEEE Transactions on Information Theory, 11, 61–70.MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1984

Authors and Affiliations

  • A. C. Harvey
    • 1
    • 2
  • C. R. McKenzie
    • 1
    • 2
  1. 1.London School of EconomicsUK
  2. 2.Australian National UniversityAustralia

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