## Abstract

State space models (SSMs) are introduced in the context of structural equation modeling (SEM). In particular, the OpenMx implementation of SSMs using the Kalman filter and prediction error decomposition is discussed. In reflection of modularity, the implementation uses the same full information maximum likelihood missing data procedures for SSMs and SEMs. Similarly, generic OpenMx features such as likelihood ratio tests, profile likelihood confidence intervals, Hessian-based standard errors, definition variables, and the matrix algebra interface are all supported. Example scripts for specification of autoregressive models, multiple lag models (VAR(p)), multiple lag moving average models (VARMA(p, q)), multiple subject models, and latent growth models are provided. Additionally, latent variable calculation based on the Kalman filter and raw data generation based on a model are all included. Finally, future work for extending SSMs to allow for random effects and for presenting them in diagrams is discussed.

Original language | English (US) |
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Pages (from-to) | 307-324 |

Number of pages | 18 |

Journal | Structural Equation Modeling |

Volume | 25 |

Issue number | 2 |

DOIs | |

State | Published - Mar 4 2018 |

## All Science Journal Classification (ASJC) codes

- General Decision Sciences
- Modeling and Simulation
- Sociology and Political Science
- Economics, Econometrics and Finance(all)