Advanced Study of Econometrics 1
Announcements |
Syllabus |
Schedule |
Problem Sets |
Homework |
Links
(4/1/13)
My office hours this year are M 12:10-12:55 and W 12:55-14:25
Objective
Understanding the theory of statistical inference,
which is necessary for any data analysis.
Textbook
Hogg, R. V.; McKean, J. W. & Craig, A. T.
Introduction to Mathematical Statistics, 7th ed.,
Pearson Education, 2013.
Related Courses
Advanced Study of Econometrics 2 (Kano)
Grading
There will be two mid-term exams and a final exam.
It is necessary to submit all homeworks (answer ALL questions).
Students can work together on homeworks, but must turn them in separately.
You must download and install
Adobe Acrobat Reader to view the course materials.
Abbreviations used in my lecture notes:
iff (if and only if),
s.t. (subject to),
s.th. (such that),
w.l.o.g. (without loss of generality),
w.r.t. (with respect to),
WTS (want to show).
Numbers in parenthesis are relevant sections in the textbook
for reading assignments.
- (4/12) Course guidance, Probability (1.1-1.4)
(slides)
- (4/15) Random Variables (1.5-1.9)
(slides)
- (4/19) Random Vectors (2.1, 2.3-2.6, 2.8)
(slides)
- (4/22) Distributions of Functions of Random Vectors (2.2, 2.7)
(slides)
- (4/26) Important Inequalities (1.10)
(slides)
- (5/2) Some Special Distributions (3.1-3.3)
(slides)
- (5/8) Normal Distributions (3.4-3.5)
(slides)
- (5/10) χ2, t, F, and Mixture Distributions (3.6-3.7)
(slides)
- (5/13) 1st Midterm
- (5/17) Asymptotic Theory (5)
(slides)
- (5/20) Sampling and Statistics (4.1)
(slides)
- (5/24) Interval Estimation (4.2-4.3)
(slides)
- (5/27) Order Statistics (4.4)
(slides)
- (5/31) Point Estimation (7.1, 6.2)
(slides)
- (6/3) Sufficient Statistics (7.2-7.9)
(slides)
- (6/7) ML Estimators (6.1, 6.4)
(slides)
- (6/10) EM Algorithm (6.6)
(slides)
- (6/14) 2nd Midterm
- (6/17) Hypothesis Testing (4.5-4.6)
(slides)
- (6/21) Likelihood Ratio Test (8.1-8.3)
(slides)
- (6/24) χ2 Goodness-of-Fit Tests (4.7, 9.8)
(slides)
- (6/28) Classical Asymptotic Tests (6.3, 6.5)
(slides)
- (7/1) Monte Carlo Method (4.8)
(slides)
- (7/8) Canceled
- (7/12) Canceled
- (7/16) Bootstrap (4.9)
(slides)
- (7/19) Canceled
- (7/22) Analysis of Variance (9.1-9.2, 9-5)
(slides)
- (7/26) Multiple Comparisons (9.4)
(slides)
- (7/29) No class
- (8/2) Final
- (4/12) Problem Set 1
- (4/15) Problem Set 2
- (4/19) Problem Set 3
- (4/22) Problem Set 4
- (4/26) Problem Set 5
- (5/2) Problem Set 6
- (5/8) Problem Set 7
- (5/10) Problem Set 8
- (5/17) Problem Set 9
- (5/20) Problem Set 10
- (5/24) Problem Set 11
- (5/27) Problem Set 12
- (5/31) Problem Set 13
- (6/3) Problem Set 14
- (6/7) Problem Set 15
- (6/10) Problem Set 16
- (6/17) Problem Set 17
- (6/21) Problem Set 18
- (6/24) Problem Set 19
- (6/28) Problem Set 20
- (7/1) Problem Set 21
- (7/16) Problem Set 22
- (7/22) Problem Set 23
- (7/26) Problem Set 24
- (Due on 5/10) Homework 1
- (Due on 5/24) Homework 2
- (Due on 6/7) Homework 3
- (Due on 6/24) Homework 4
- (Due on 7/16) Homework 5