Panda-metrics-2024-25
Содержание
What-about
Course whitepaper
Course goals
侍には目標がなく道しかない [Samurai niwa mokuhyō ga naku michi shikanai]
A samurai has no goal, only a path.
Telegram channel, Telegram chat
Lecture and class hand-made (with love) video recordings + official videos ya-folded
Grading
Semester-1 grade = 0.2 HA-1 + 0.4 Midterm-Exam1 + 0.4 Exam-Semester1.
Midterm-Exam1 is scheduled in Module 2.
Grades for HA-1, Midterm-Exam1 and Exam-Semester1 are integers from 0 to 100.
Semester-2 grade = 0.2 HA-2 + 0.4 Midterm-Exam2 + 0.4 Exam-Semester2.
Grades for HA-2, Midterm-Exam2 and Exam-Semester2 are integers from 0 to 100.
Final course grade = 0.5 Semester-1 grade + 0.5 Semester-2 grade
When necessary 0-100 grades are converted into 0-10 grades using division by 10 and standard rounding.
Midterm 1: 12th November, 18:10.
Home assignments
You have 4 honey weeks for the entire course. All home assignments of the first semester have equal weights. All home assignments of the second semester have equal weights.
Exams
Samurai diary
2024-09-02, lecture 1: Derivation of beta hat in the cases of a very simple regression and multiple regression.
2024-09-09, lecture 2: Geometry of regression. Fitted vector is the projection of y-vector onto the Span of regressors. Hat-matrix: definition, simple properties. SST, SSE, SSR: definition, Pythagorean theorem: SST = SSE + SSR.
2024-09-16, lecture 3: Conditional expected value, conditional variance. Statistical assumptions for simple regression. Expected value of beta hat for simple regression. Statistical assumptions for multiple regression. Expected value of beta hat for multiple regression. Variance of beta hat for multiple regression.
2024-09-23, lecture 4: Properties of conditional variance and conditional covariance in matrix form. Gauss-Markov assumptions. Hat matrix is proportional to conditional variance of forecasts. Proof of Gauss-Markov theorem through Pythagoras.
- Geometry in econometrics
2024-09-30, lecture 5: Consistency of beta hat in matrix form. Inconsistency of beta hat in a simple regression with measurement error in regressor.
2024-10-07, lecture 6: Estimating variance of random error: unbiasedness of SSRes / (n - k), consistency of SSRes / (n - k).
2024-10-14, lecture 7: Herschel-Maxwell assumptions give us normal distribution. Chi-squared distribution as squared length of projection of standard normal vector onto d-dimensional subspace. Proof that t-statistic in multivariate regression has t-distribution.
- 3b1b Herschel-Maxweel assumptions and multivariate normal
2024-10-21, lecture 8: Bootstrap before regression: naive bootstrap, t-statistic bootstrap. Regression with bootstrap: pair bootstrap, wild bootstrap (+1/-1 version).
- Tim Hesterberg, What Teachers Should Know about the Bootstrap: fun and enjoyable introduction to bootstrap!
- Russell Davidson, James G. MacKinnon, Bootstrap methods in Econometrics
Classes
Class notes
Maria Kirillova notes
2024-09-06, class 1: 1.1, 1.2 from MPro
2024-09-13, class 2: 3.2, 3.10, 3.7 from MPro
2024-09-20, class 3: 5.5 from MPro, derivation of variance of slope estimate for simple regression.
2024-09-27, class 4:
2024-10-04, class 5:
2024-10-11, class 6: confidence interval for beta, hypothesis test for beta, test of equality of two betas, confidence interval for conditional expected value of forecast.
2024-10-18, class 7: F-test. F-test for regression significance. Constructing restricted model. Chow test.
2024-11-01, class 8: Calculation probabilities, expected values, variances and covariances for naive bootstrap.
Sources of Wisdom
CausML: Causality in ML book with python and R code
MPro-en: Problem set for classes (translation in progress)
MPro-ru: Problem set for classes (in Russian)
