Estimation of Time Series Models in the Presence of Missing Data

Abstract

A method is proposed for the estimation of models for discrete time series in the presence of missing data. Some justification is given for the use of this method over alternatives; the choice of estimator is likely to be governed by the pattern of missing data, the nature of the time series model, and computational considerations. The method's performance in estimating simple models is studied by simulations, and it is applied to a time series of pollution levels containing some missing observations.

Key Words:

• Time series analysis
• Missing data
• Autoregressive moving-average models
• Spectral estimation
• Pollution data