Panda-metrics-2024-25 — различия между версиями
Bdemeshev (обсуждение | вклад) |
Bdemeshev (обсуждение | вклад) (→Samurai diary) |
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| Строка 50: | Строка 50: | ||
2024-09-16, lecture 3: Conditional expected value, conditional variance. Statistical assumptions for simple regression. Expected value of beta hat for simple regression. | 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. | 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. | ||
| + | |||
| + | More: Geometry in [https://raw.githubusercontent.com/olyagnilova/gauss-markov-pythagoras/master/paper.pdf econometrics] | ||
=== Classes === | === Classes === | ||
Версия 23:27, 24 сентября 2024
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.
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
Class notes
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.
More: Geometry in econometrics
Classes
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.
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)
