Explain why these statements are false, 1. Heteroscedasticity engenders bias in

Question

Explain why these statements are false,

1. Heteroscedasticity engenders bias in regression coefficients.

2. The analytical situations that cause a probit model to be appropriate make a logit model unusable.

3. OLS is inappropriate when X variables are binary.

4. A strong instrument used in the first stage of 2SLS increases the impact of endogeneity between X in Y in the second stage regression.

5. If the standard errors of OLS coefficients are known to be exaggerated due to specific violations in OLS assumptions, an indication that one can reject the null hypothesis should be disregarded.

Explanation / Answer

1 ans)   Heteroscedasticity engenders bias in regression coefficients.because it can cause only ols estimates of the variance with coefficients to be biased.

2 ans) probit model to be appropriate make a logit model unusable. because the depend variable can take only two variables

3 ans) OLS is inappropriate when X variables are binary because the assumption of homoscedasticity is untenable, the dependent variable is bounded

4 ans) no because it actually decreases not increases so the impact doesnt vary

5 ans)   indication that one can reject the null hypothesis should be considered not disregarde because one can say the hypothesis is rejected or not

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