Econ 327 (BIE) Introduction to Empirical Methods 2014

Econ 327 (BIE)
Introduction to Empirical Methods
2014-15 Winter Session
Vadim Marmer
Office: BuTo 1016
Phone: (82)2-8217
Email: [email protected]
Time and Location: Tuesdays and Thursdays 11:00 - 12:20, Room107, University Centre Lower
Level, 6331 Crescent Road
Office Hours: Tuesdays 3-4PM or by appointment
Course web-page: http://faculty.arts.ubc.ca/vmarmer/econ327
Discussion groups: 1) Tuesdays 5:00-6:00; 2) Wednesdays 6:00-7:00; 3) Thursdays 5:00-6:00.
TAs: Anton Laptiev, [email protected].
Office hours: After the discussion groups or by appointment.
Textbook: Hogg, R. V., E. Tanis, and D. Zimmerman (2015): Probability and Statistical Inference,
9th Edition, Pearson, Boston (required); see also Pearson Math and Stats Resources.
Course Description: This course is a formal introduction to Probability and Statistics for BIE
students. No prior knowledge of probability and statistics is required as all concepts will be developed from the ground up. However, students are expected to have a good calculus background,
since almost all results will be given with complete proofs (in addition to discussing the intuition
behind them). In the first part of the course, we will cover a range of topics from the basics of
probability theory to the properties of statistical distributions such as moments and quantiles. A
special emphasize will be given to the concept of conditional distributions. In the second part of
the course, we will discuss estimation theory and statistical inference including maximum likelihood
and method of moments estimators and their properties, confidence intervals, hypothesis testing,
power of tests and their optimality, and multiple hypotheses testing. If time permits, we will cover
certain topics in regression analysis. Otherwise, regression analysis will be covered in Econ 328,
which is a continuation of this course.
Besides the required textbook, students may find useful the following textbook: Amemiya, T.
(1994): Introduction to Statistics and Econometrics, Harvard University Press, Cambridge, Massachusetts. It covers the same material as Hogg, Tanis, and Zimmerman and is written for students
majoring in economics. However, the book by Amemiya is more brief than Hogg, Tanis, and Zimmerman.
On the course’s web-page, I will distribute my lecture notes, which assemble material from
numerous sources, books and lecture notes. While the lecture notes will contain all the required
material, students should not ignore reading assigned chapters from Hogg, Tanis, and Zimmerman.
Besides numerous examples and additional details, the textbook often supplies somewhat different
treatment of the same material. In my experience, looking at the same topic from different perspectives is often essential for its understanding at a deeper level. Assigned chapters from Hogg, Tanis,
and Zimmerman will be listed on the web-page.
There will be weekly problem sets with analytical and practical questions. Practical questions
will require handling and analyzing data using statistical software packages. The recommended
software this course is Stata. It is a commercial software program and available for purchase at
special GradPlan pricing (student-pricing), see the following link: http://goo.gl/8kcnWq. Many
useful Stata tutorials can be found online. See, for example, http://data.princeton.edu/stata/ and
http://stataproject.blogspot.com/. Interested students may consider using R instead of Stata. R is
free, and there are numerous resources available online for learning it.
Grading: Assignments (20% of the final grade), midterm exam (30%), final exam (50%).
Tentative schedule:
Weeks of September 2, 9: Basics of probability: Sample spaces, set theory, probability function,
counting techniques; Conditional probability, independence, Bayes’ theorem. Readings: Ch
1, Appendix D.1.
Week of September 16: Discrete distributions: random variables, probability mass function (PMF),
cumulative distribution function (CDF), moments, moment generating function (MGF); Bernoulli,
binomial, Poisson and geometric distributions. Readings: Ch 2.
Week of September 23: Continuous distributions: probability density function (PDF), CDF,
moments and MGF; percentiles (quantiles); Exponential, χ2 , uniform and normal distributions. Readings: Ch 3.1-3.3.
Week of September 30: Multivariate and bivariate distributions: joint PMF, covariance and
correlation, conditional distributions, conditional expectation, joint PDF, bivariate normal
distribution. Readings: Ch 4.
Week of October 7: Distributions of functions of random variables; order statistics. Readings:
Ch 5.1, 5.3, 5.4, 5.5; 6.3.
Week of October 14, 21: Estimation problem, estimator, normal location-scale model, maximum likelihood estimator (MLE), method of moments estimator, simple regression model.
Readings: Ch 6.4, 6.5. Midterm on October 21.
Week of October 28: Asymptotic theory: convergence in probability, consistency, law of large
numbers (LLN), convergence in distribution, central limit theorem (CLT), asymptotic normality. Readings: Ch 5.6, 5.8, 5.9.
Weeks of November 4, 11: Confidence intervals and hypothesis testing: confidence intervals,
hypothesis testing problem, test, size and power of a test, one-sided and two-sided tests,
p-values, multiple hypotheses testing and Bonferroni tests. Readings: Ch 7.1, 7.6, 8.1, 8.5.
Week of November 18: Neyman-Pearson Lemma and likelihood ratio tests. Readings: Ch 8.6,
8.7.
Week of November 25: Topics in regression analysis. Readings: TBA.
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