论文信息 - Missing Observations in Dynamic Econometric Models: A Partial Synthesis

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.

Andrew Harvey | Colin McKenzie | A. Harvey | C. McKenzie

[1] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[2] Robert Kohn,et al. Fixed interval estimation in state space models when some of the data are missing or aggregated , 1983 .

[3] R. Engle,et al. A One-Factor Multivariate Time Series Model of Metropolitan Wage Rates , 1981 .

[4] A. Jazwinski. Stochastic Processes and Filtering Theory , 1970 .

[5] H. Hartley. Maximum Likelihood Estimation from Incomplete Data , 1958 .

[6] B. Anderson,et al. Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[7] A. Harvey. Time series models , 1983 .

[8] E. G. Drettakis. Missing Data in Econometric Estimation , 1973 .

[9] Fred C. Schweppe,et al. Evaluation of likelihood functions for Gaussian signals , 1965, IEEE Trans. Inf. Theory.

[10] Barr Rosenberg,et al. The Analysis of a Cross-Section of Time Series by Stochastically Convergent Parameter Regression , 1973 .

[11] J. Sargan,et al. Missing Data in an Autoregressive Model , 1974 .

[12] Adrian Pagan,et al. Some identification and estimation results for regression models with stochastically varying coefficients , 1980 .