Autoregressive Models with Latent Variables

Observations of time series often are corrupted by noise. This has a negative influence on the quality of estimated parameters of autoregressive models. A new technique is presented here. The individual, not directly observable, samples of the corrupted time series are modeled and estimated as latent variables. The estimation-process leads to the iterative solution of two non-linearly coupled systems of linear equations. Adaptations for missing values, additive and innovative outliers are described. Some applications are presented.