Estimating Missing Observations in Economic Time Series

Abstract

Abstract Two related problems are considered. The first concerns the maximum likelihood estimation of the parameters in an ARIMA model when some of the observations are missing or subject to temporal aggregation. The second concerns the estimation of the missing observations. Both problems can be solved by setting up the model in state space form and applying the Kalman filter. Key Words: Autoregressive-integrated-moving average processesKalman filterMaximum likelihood estimationMissing observationsSmoothingTemporal aggregation