Forecasting, time series, and regression

Bruce L. Bowerman, Richard T. O'Connell, Anne B. Koehler

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The content can be very very hard to grasp at first, but once you understand the whole narrative of the course, it becomes clear as water. Examinations can be challenging but fair. The lecturer, David U., did a decent job.

Anonymous, Semester 2, 2019

This was a rewarding econometrics subject. The lecturer, David, although he does speak very softly, he was approachable and easy-going. As this was a pilot unit of study, the topics were determined throughout the semester, but they were well chosen: probability, time series regression (trends, seasonality, autoregression, serial correlation, unit roots and cointegration) and limited dependent variable regression (LPM, probit and logit models, maximum likelihood and Tobit model). A little bit daunting at first, but once you sit down and understand it, I could see how topics were interrelated with each other. In tutorials, we had a good mix of theoretical and calculation questions, as well as computer work on R. There are also three homework assignments to hand in during a lecture; very good on demonstrating what I had learned. All in all, much better than ECMT2150, for sure.

Anonymous, Semester 2, 2015