1/22: Introduction to the course
1/24: Statistics review I: random variables, the density function, expectations, and variance
1/29: Statistics review II: conditional expectation, covariance, and correlation
1/31: Simple regression I: estimation in descriptive analysis
2/5: Simple regression II: estimation in causal analysis and forecasting
2/7: Simple regression III: algebraic properties of OLS estimators, functional form, and units of measurement
2/12: Simple regression IV: Unbiasedness of the estimator and omitted variable bias
Here is my lecture note before midterm 1
(Coming soon!)
2/14: Multiple regression I: adding one regressor and estimating model
2/19: Multiple regression II: goodness of fit, the adjusted R-squared, and functional forms
2/21: Midterm I
2/26: Multiple regression III: unbiasedness, omitted variables, and variance of OLS estimators
3/1: Distribution of OLS estimators: normal random variables and distribution of OLS estimators
3/5:
3/8:
Here is my lecture note before midterm 2
Homework 3 + log file (available after Feb. 5)