We are now starting a 5 lecture block on Gaussian state-space models.
Lectures 1 & 2: building blocks for analysis of multivariate time-series data with observation error, structure, and missing values
Lectures 3-5: Specific applications: covariates, dynamic factor analysis, dynamic linear models
- Properties of time series data
- AR and MA models: \(x_t = b_1 x_{t-1} + b_2 x_{t-2} + e_t\)
- Today: State-space models (observation error and hidden random walks)