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Keywords:

  • Missing data;
  • Kalman filter;
  • EM algorithm;
  • forecasting;
  • maximum likelihood

Abstract. An approach to smoothing and forecasting for time series with missing observations is proposed. For an underlying state-space model, the EM algorithm is used in conjunction with the conventional Kalman smoothed estimators to derive a simple recursive procedure for estimating the parameters by maximum likelihood. An example is given which involves smoothing and forecasting an economic series using the maximum likelihood estimators for the parameters.