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SDS 389 SDSĀ 389. Time Series and Dynamic Models. 3 Hours.

Exploration of the general class of state-space models, or dynamic models. Emphasis is placed on the implementation and use of the models presented, with applications focused on the social sciences. Topics include dynamic regression models, the Kalman filter, time series models, multivariate time series models, conditional variance models, Markov chain Monte Carlo algorithms for state-space models, and particle filters. Three lecture hours a week for one semester. Statistics and Data Sciences 389 and Statistics and Scientific Computation 389 may not both be counted. Prerequisite: Graduate standing; Economics 392M (Topic 19), Statistics and Data Sciences 384 (or Statistics and Scientific Computation 384), or the equivalent; and coding skills in R, Matlab, or Stata.