QE Flashcards
F-Test Formula
Careful on your definiton of K
[n-k-1/q] [TSS-RSS/TSS]
^all unrestricted model.
RSSrest = TSSunrest
k is number of regressors, q is restrictions
Speciffy it as the homoskedastic only F-test
Confidence Interval Values
90% - 1.645
95% - 1.96
99% - 2.58
Standard Error Forumla
sigma/sqrt(n)
Variance formulae
Var(X)=E[(X-mu )^2]
= E[X^2] - E[X]^2
Covariance formulae
E(XY) - E(X)E(Y)
= E(X-E(X))(Y-E(Y)
R^2 Formula
1 - (SSR/SST)
Adjusted R^2
1 - [(n-1)/(n-k-1)] [SSR/SST]
LATE Assumptions
MIRE
like quagMIRE
LATE Definition
Average causal effect on COMPLIERS
TOT
Treatment effect On the Treated
WEIGHTED average of the causal effect on COMPLIERS and the causal effect on ALWAYS TAKERS
TOU
Treatment effect On the Untreated
WEIGHTED average of the causal effect on COMPLIERS and the causal effect on NEVER TAKERS
Bloom Result
If no never takers, LATE = TOT
5 Threats to internal validity
HIPCA
Individualistic treatment response (each person’s outcome depends only on his own treatment), contamination, Hawthorne effect, placebo, attrition
4 Threats to external validity
S A S S
Sampling, spillover effects, assignment differences, short durations (surrogate outcomes).
Type 1 Error
Incorrect rejection of a true null.
False positive.
Type 2 Error
Incorrectly accepting a false null.
False negative
IV Assumptions
Exogeneity of instrument (good as randomly assigned and exclusuion), and relevance
3 in total for QE
Testing IV independence assumption
Not directly testable.
- Distribution of covariates
- Baseline information
Derive Omitted Variable Bias formula
SRBeta = LRBeta + OVB
OVB = gamma (Cov(X,OmittedVar)/Var(OmittedVar))
OVB only does not equal to zero if
1. omitted var is correlated with outcome
2. omitted var is correlated with other instrument.
Autocovariance
Cov ( Yt , Yt-1 )
What is the summation equation?
Autocorrelation
Cov ( Yt , Yt-j ) / var (Yt)
Dickey-Fuller Test
Test if unit root is present.
I.e. H0: Beta1 = 0
WHAT DOES THIS DO
Chow Test
Testing for structural breaks
How is this done?
Spurious regression
Regression that provides evidence of a non-existent relationship between two variables. I.e. two random walks regressed on each other.
