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Time Series Analysis II

EPPS 7371

School of Economic, Political and Policy Sciences

This course introduces intermediate and advanced methods for the analysis of social science time series data. After reviewing core time series concepts such as stationarity and cointegration, the course considers topics such as vector autoregression and vector error correction models, simultaneous equation and structural time series models, regime switching models, non-Gaussian and nonlinear models, and state space representations. Both frequentist and Bayesian approaches to modeling time series processes are employed. Data analyses are implemented using widely available software packages such as R, RATS and Stata. 3 credit hours.

Prerequisite: EPPS 7370 or instructor consent required.

Offering Frequency: Based on student interest and instructor availability

Grades: 23

Median GPA: A

Mean GPA: 3.870

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