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Reference repository for the Econometrics with Python (23/24/1) course. This repository will contain the links, PDFs, and codes required for the weekly preparation. Weekly tests are held on Wednesdays between 19:00-20:30, and consultations are on Fridays.

Test results

Theory file(s) available in the theory forlder.

Required for the 1st theoretic test:

• Properties of Expected Value (2)
• Properties of Variance (3)
• Variance decomposition of non-independent random variables (1)
• Studentization / Standardization (1)

Required for the 2nd theoretic test:

• Convergences (4)
• Slutsky's Theorem (2)
• Consistency (1)

Required for the 3rd theoretic test:

• Laws of Large Numbers (2)
• Law of the iterated logarithm(1)
• Central Limit Theorem (1)
• Delta method (1)
• Asymptotically Efficient Estimator (1)

Required for the 4th theoretic test:

• Gauss-Markov assumption (5) video with no perfect overlap
• OLS unbiasedness (1) only equation, no proof
• OLS consistency (1) only equation, no proof
• Gauss-Markov Theorem (1)

Required for the 5th theoretic test (review some major theorems and assumptions):

• Week Law of Large Numbers (1)
• Central Limit Theorem (1)
• Delta method (1)
• Gauss-Markov assumption (5) video with no perfect overlap

Required for the 6th theoretic test:

• Z-value (1)
• T-value (1)
• 3 steps of T-test (1)
• Small sample Wald test (1)
• Centered R-squared and adjusted R-squared values (2)

Required for the 7th theoretic test:

• Implications of heteroscedasticity (4)
• Gauss-Markov assumptions in case of generalized regression model (5)
• Finite-sample properties of GLS (3)

Required for the 8th theoretic test:

• Likelihood function (2)
• Maximum Likelihood Estimator (1)
• Log-likelihood function of Normal distribution (1)
• Properties of MLE (4)
• Efficiency of MLE (1)

Required for the 9th theoretic test:

• Bootstrap Theory (2) (not the entire slide, just the box)
• Empirical Bootstrap steps (4)
• Mode's of confidence interval estimation with bootstrap (3)
• Types of bootstraps mentioned in the slides (6) (names are enough)
• Gauss-Markov Theorem (1)

Python Classes (If the link does not work, open in * incognito mode*.)

Matplotlib Axes interface

Matplotlib Axes interface 2

Pandas User Guide

SciPy optimization module

Example notebook for SciPy optimization

Example for bootstrapping CI in Linear Regression

Kaggle example for bootstapping CIs