Quant Shit Flashcards
Conditional heteroskedasticity is
residual variance related to level of X’s
Serial correlation is
correlated residuals
Multicollinearity is
two or more X’s are correlated
Effect of conditional heteroskedasticity
Type I errors
high t stat, caused by low std errors
Effect of serial correlation
Type I errors
positive correlation
Effect of multicollinearity
type II errors
Detection of conditional heteroskedasticity
Breusch-Pagan Test
Chi-Square Test
Detection of serial correlation
Durbin-Watson test
Detection of multicollinearity
Conflicting t and F stats
Correlations among ind variables if k=2
Correcting conditional skedasticity
white-correct std errors
Correction serial correlation
Hansen method
Correcting multicollinearity
Drop a correlated variable
Functional Form Misspecifications
- important variables omitted
- variables not transformed properly
- data pooled improperly
Time-Series Misspecification
- X is lagged Y with serial correlation present
- Forecasting the past
- Measurement error
Probit model
estimates probability of default given values of X based on normal dist
Logit Models
estimates probability of default given values of X based on logistic dist (computationally easier than normal dist).
Logistic dist NOT logarthimic
Discriminant models
produces a score or rank used to classify into categories
ex- bankrupt, not bankrupt
Economic Significance
not significant just because of statistical significance
-commissions, taxes, risk, etc.
If a time series is mean reverting
the value of the dependent variable tends to fall when above its mean; and rise when below its mean
Mean Reverting Level Formula
b0/ (1 - b1)
Forecasting Accuracy of ARCH measured by
root of mean squared error.
Use model with lowest RMSE based on out-of-sample forecasting
Without a mean reverting level, the time series is
non-stationary
Dickey-Fuller Tests for
unit root
Dickey Fuller Test method
subtract x(t-1) from both sides; first differencing where g1 = (b1 - 1)
If there is a unit root in AR(1) model , g1 will be 0.
