10.) Heteroskedasticity Flashcards
Heteroskedasticity is the violation of…
Classical Assumption V, which states that the observations of the error term are drawn from a distribution tha thas a constant variance.
Heteroskedasticity is important because…
OLS, when applied to heteroskedastic models, is no longer the minimum variance estimator (it still is unbiased however)
Heteroskedasticity is more likely to take place in…
cross-sectional models than in time-series models.
Heteroskedasticity can be divided into…
pure and impure versions
Pure heteroskedasticity is caused by…
the error term of the correctly specified equation
Impure heteroskedasticity is caused by …
a specification error such as an omitted variable
Pure heteroskedasticity refers to …
heteroskedasticity that is a function of the error term of a correctly specified regression equation
Pure heteroskedasticity occurs when…
classical assumption V, which assumes tha the variance of the error term is constant, is violated in a correctly specified equation
In homoscedasticity the error term has a constant variance, so..
the observation are continually drawn from the same distribution
The larger disparity between the size of observations of the dependent variable in a sample…
the larger the likelihood tha the error term observations associated with them will have different variances and therefore be heteroskedastic.
Discrete Heteroskedastic can be grouped visually into …
these two distributions “wide” & “narrow”
The variable Z is called a …
proportionality factor because the variance of the error term changes proportionally to the square of Zi.
The higher the value of Zi…
the higher the variance of the distribution of the ith observation of the error term.
Heteroskedastic distribution gets wider as…
Z increases
Homoskedastic distribution…
maintains the same width no matter what value Z takes.
Heteroskedasticity is likely to occur in cross-sectional models because of..
the large variation in the size of the dependent variable involved.
Heteroskedasticity can occur in a …
time-series model with a significant amount of change in the dependent variable
Heteroskedasticity can occur in any model, where…
the quality of data collection changes dramatically within the sample.
As data collection techniques get better, the …
variance of the error term should fall because measurement errors decrease in size, so should the variance of the error term
Impure Heteroskedasticity…
is caused by an error in the specification, such as an omitted variable
An omitted variable can cause a heteroskedasticy error term because…
the portion of the omitted effect not represented by one of the included explanatory variables must be absorbed by the error term
Pure heteroskedastcity does not…
cause bias in the coefficient estimates
Heteroskedasticty typically causes OLS to …
no longer be the minimum-variance estimator (of all the linear unbiased estimators)
Heteroskedasticity cause the OLS estimates of the SE(B)S to be…
biased, leading to unreliable hypothesis testing.
What sort of bias does heteroskedasticity tend to cause?
SE(B) is negative, OLS underestimates the size of the standard errors o the coefficients
What’ll happen to hypothesis testing if OLS underestiamtes the SE(B)s and therefore overestimates the t-scores
The “too low” SE(Bhat) will cause a “too high” t-score for a particular coefficient
The first thing to do if the Park test or the White test indicates…
the possibility of heteroskedasticy is to examine the equation carefully for specification errors
If there are no obvious specification errors, the heteroskedastcity is probably,..
pure in nature
The most popular remedy for heteroskedasticity is …
heteroskedastcity correct standard errors
heteroskedastcity correct standard errors adjust for…
the SE(B) for heteroskedasticty while using the OLS estimates of the slope coefficients.
The heteroskedastcity correct standard errors are…
SE(B)s that have been calculated specifically to avoid the consequences of heteroskedasticity.
The HC procedure yields an…
estimator of the standard errors that, while they are biased, are generally more accurate than the
