8. Heteroskedasticity Flashcards

1
Q

what does homoskedasticity mean?

A

They are, you has the same variance, given any of the explanatory variables

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2
Q

Why is the variance so important?

A

Because it gives you a measure of your precision

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3
Q

Why is the homoskedasticity hard to satisfy in the wage and education example?

A

Homoskedasticity would imply that the variability in wage about its mean is constant across all education levels

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4
Q

How does the estimation of the coefficient change under heteroskedasticity?

A

E(B^)= B still holds and is still unbiased but you can no longer derive the variance in the same waty

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5
Q

What does not change under heteroskedasticity for OLS?

A
  • OLS is still unbiased and consistent under heteroskedasticity
  • The interpretation of R^2 does not change
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6
Q

What DOES change under heteroskedasticity for OLS?

A
  • Heteroskedasticity invalidates variance, formulas for OLS estimators
  • the usual F tests and t-tests are not valid under heteroskedasticity
  • OLS is no longer the best linear and biased estimator
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7
Q

What assumptions need to be satisfied for an estimator to be unbiased?

A

Linear in Parameters
Random sampling
Sample variation in the explanatory variable
Zero conditional mean

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8
Q

What are robust standard errors?

A

Another way of calculating the variance so that it still holds under heteroskedasticity. They hold under homo and heteroskedasticity

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9
Q

What are the limitations to using robust standard errors?

A

Under robust standard errors, no longer directly follows a t-distribution but this does not matter under a large sample.

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10
Q

What is considered a large sample?

A

More than 120

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11
Q

What is a good rule of thumb about the f-value difference between using the robust se and regular se?

A

If there is strong heteroskedasticity differences may be larger to be on the safe side, it is advisable to always compute, robust standard errors

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12
Q

How do you compute robust standard errors in stata?

A

, vce robust

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13
Q

How can you get heteroskedasticity robust f-tests in stata?

A

Using the command test

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14
Q

What is an auxiliary regression?

A

Auxiliary regressions are regressions that are not part of your main model

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15
Q

What are the issues with using a white test?

A

The degrees of freedom become an issue particularly with large models

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