Econometrics T1

Gaus Markov assumptions

formulae for SST, SSR, SSE, R², and relationships

omitted variable bias

leads to invalid t and f tests, calculate bias to check?

three components of OLS variance

error variance, SST, R²

error variance formula

CLM assumption and problem

Gauss markov + normality assumption, problem that it assumes all unobserved factors affect y in a separate additive fashion

t test formula and when to reject

H₀ rejected if t>c

CLT

if IID then normally distributed with var= s.d²/n

Frisch-Waugh Theorem (beta hat=…..)

R² formula

variance formula

law of iterated expectation

t test

F test

Chow test

log form changes

why do we use robust standard errors

control for heteroskedasticity

simultaneity bias

two variables influence each other at the same time, have endogeneity

over specifying

including too many variables especially ones that are not relevant

adjusted R²

use to see if a model with more variables gives better fit, use when lots of variables

consistent OLS estimate

condition to use IV

covariance formula