- Schedule of Classes - April 16, 2021 7:14PM EDT
- Course Catalog - April 16, 2021 7:15PM EDT
Course information provided by the Courses of Study 2020-2021.
The course introduces students to Bayesian time series methods. Students will learn how to make likelihood-based inference about unobserved quantities, e.g. model parameters, policy impacts or future outcomes, conditional on the observed data. Applications include structural vector autoregressions, state space models and linearized dynamic stochastic general equilibrium macro models. Student will become familiar with numerical posterior simulation techniques such as Gibbs sampling and the Metropolis-Hasting algorithm. The course is useful for any students interested in empirical work that involves time series and/or structural likelihood-based estimation.
When Offered Spring.
Course Attribute (EC-SAP)
Seven Week - Second.
Credits and Grading Basis
2 Credits Stdnt Opt(Letter or S/U grades)
Class Number & Section Details
- TROnline Meeting
- Mar 29 - May 14, 2021
Instruction Mode: Online
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