Fall 2019 - ECON 837 G100
Econometrics I (4)
Class Number: 1024
Delivery Method: In Person
Course Times + Location:
Mo 12:30 PM – 2:20 PM
WMC 3611, Burnaby
We 12:30 PM – 1:20 PM
WMC 3611, Burnaby
Prerequisites:ECON 835 or equivalent.
Develops a foundation for econometric theory and applied econometrics. Topics may include an introduction to measure and probability theory, integration and mathematical expectations, stochastic limit theory, asymptotic theory, mathematical statistics, multiple linear regression, and an introduction to GMM and maximum likelihood estimation.
This course is an introduction to probability theory and statistical inference designed for first year economics Ph.D. students. Students are expected to have taken calculus, linear algebra and some introductory probability and statistics.
- Probability: Probability Spaces, Random Variables, Random Vectors, Distributions, Transformations, Special Distributions of the Discrete Type and of the Continuous Type;
- Statistics: Sample analogue estimation, maximum likelihood, Hypothesis testing;
- Asymptotics: Modes of Convergence, Law of Large Numbers, and Central Limit Theorem;
- Midterm exam 40%
- Final exam 60%
- There will be weekly problem sets that are discussed in the lecture.
Casella and Berger, 2001, “Statistical Inference”, 2nd Edition, Cengage Learning.
Cameron and Trivedi, 2005, “Microeconometrics: Methods and Applications”, Cambridge University Press.
Hogg, Craig and McKean, 2018, "Introduction to Mathematical Statistics," 8th edition, Pearson.
Graduate Studies Notes:
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ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS