Department of Statistics

STATS 326 Applied Time Series Analysis

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Below description edited in year: 2018

Points: 15

Prereqs: 15 points from STATS 201, 207, 208, BIOSCI 209 or ECON 221

Credit: Final exam 50%; coursework 50% (1 test worth 15% and assignments worth 25% and in class quizzes and/or weekly Canvas Quiz worth 10%), 50% in final exam to pass.

For Advice: Mike Forster (Email: | extn: 88759)

Taught: Summer School City, First Semester City

Website: STATS 326 website

STATS 326 covers Time Series data, with an emphasis on computer based analysis and reporting the results of analyses.

Topics studied include: Time series data, non-stationary time series models, stationary time series models, differencing of non-stationary time series and an introduction to some advanced topics in time series analysis (Multiple Time Series Regression; Arch & Garch; Panel Data; Random Walks, Spurious Regression, Unit Root tests and Co-integrated Time Series Models). The approach will be largely non-mathematical and practical, with an emphasis on applications using R and an appreciation of the problems associated with modelling time series data.

Although every reasonable effort is made to ensure accuracy, this information for the course year (2019) is provided as a general guide only for students and is subject to alteration. All students enrolling at the University of Auckland must consult its official document, the University of Auckland Calendar, to ensure that they are aware of and comply with all regulations, requirements and policies.

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