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State Space Methods for Latent Trajectory and Parameter Estimation by Maximum Likelihood

Chapter

Abstract

We review Kalman filter and related smoothing methods for the latent trajectory in multivariate time series. The latent effects in the model are modelled as vector unobserved components for which we assume particular dynamic stochastic processes. The parameters in the resulting multivariate unobserved components time series models will be estimated by maximum likelihood methods. Some essential details of the state space methodology are discussed in this chapter. An application in the modelling of traffic safety data is presented to illustrate the methodology in practice.

Keywords

State Space Model Unobserved Component Seat Passenger State Space Method Rear Seat 
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Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of EconometricsVU University AmsterdamAmsterdamThe Netherlands

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