Missing Observations in Dynamic Econometric Models: A Partial Synthesis
Generate an AI Snapshot to get a quick, structured summary of this paper.
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A number of methods for carrying out the maximum likelihood estimation of a dynamic econometric model with missing observations are examined 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.
Abstract
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.