ECON 468 - Econometrics 1 - Honours (3 credits)
- School McGill University
- Faculty Faculty of Arts
- Description The statistical basis of econometric modelling and treatment of the linear regression model; simple time series models; procedures for inference in linear cases; an introduction to methods for dealing with endogeneity and non-constant variance.
- Conditions of Access Prerequisite(s): ECON 257D1/D2 or permission of the instructor.
Restriction(s): Not open to students who have taken or are taking ECON 467D1/D2
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Anonymous
Additional Comments:
I had Dufour, not Davidson. At first it seemed really fucked up but the midterm & final were a breeze. Half the questions were recycled from assignments and past finals. Definitely memorize the Gauss-Markov theorem but otherwise you just need to know results & properties, not proofs or how to derive everything. Expect very modest curving. Assignments are hard but very instructive. A very goofy and, in my opinion, likeable guy. That said, he has a very oblique lecture style. Some found him frustrating; he doesn't explain things very thoroughly and all of the proofs use matrix algebra or calculus, no geometric reasoning as in the textbook. A lot more interesting and useful than either of the first year Honours stats classes, but much more challenging.
I had Dufour, not Davidson. At first it seemed really fucked up but the midterm & final were a breeze. Half the questions were recycled from assignments and past finals. Definitely memorize the Gauss-Markov theorem but otherwise you just need to know results & properties, not proofs or how to derive everything. Expect very modest curving. Assignments are hard but very instructive. A very goofy and, in my opinion, likeable guy. That said, he has a very oblique lecture style. Some found him frustrating; he doesn't explain things very thoroughly and all of the proofs use matrix algebra or calculus, no geometric reasoning as in the textbook. A lot more interesting and useful than either of the first year Honours stats classes, but much more challenging.