Tag: ols
Concepts
Interview Questions
- What "BLUE" really promises about OLS
- Autocorrelated errors break your standard errors, not your coefficients
- Clustered errors and why naive standard errors mislead
- Multicollinearity versus heteroskedasticity: what each one actually breaks
- Heteroskedasticity breaks your standard errors, not your coefficients
- Put a number on the variance inflation
- The dummy variable trap: when collinearity is perfect
- Diagnose the sign flip and the huge standard errors
- Correlated regressors, what actually breaks?
- What heteroskedasticity does to OLS
- Omitted-variable bias and why more data won't save you
- Autocorrelated errors and inflated t-stats
- Multicollinearity, what it does and does not do
- OLS assumptions, which one buys you what?
- Deriving the least-squares line by hand
- Sign and size the bias from omitting market beta
- The classic wage, schooling, and ability trap
- Does throwing in an irrelevant regressor bias anything?
- Attenuation, when your regressor is measured with noise
- Omitted variable bias, derive the formula
- Omitted variable bias: sign and size
- Reading coefficients when there is an interaction term
- Interpreting dummy variables and the dummy-variable trap
- Reading a log-log market-impact coefficient as an elasticity
- Interpreting multiple regression coefficients