Question 3 If data is missing for completely random reasons (i.e., not related to X or...
Question 3
If data is missing for completely random reasons (i.e., not related to X or Y), then this leads to:
Question 3 options:
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A bias in the OLS estimator. |
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A reduction in sample size. |
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An increase in the variance of the OLS estimator. |
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Both (b) and (c). |
Question 4
Consider the linear probability model Yi = β0 + β1Xi + ui. Assume E(ui|Xi)=0. Which of the following statements are true?
Question 4 options:
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The predicted value of the dependent variable will always be between 0 and 1. Thus, the OLS estimator of β1 is unbiased. |
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The predicted value of the dependent variable can be greater than 1 or less than 0. Thus, the OLS estimator of β1 is biased. |
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The predicted value of the dependent variable will always be between 0 and 1. Thus, the OLS estimator of β1 is biased. |
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The predicted value of the dependent variable can be greater than 1 or less than 0. This does not mean the OLS estimator of β1 is biased. |
Homework Answers
Answer: 3:
If data is missing for completely random reasons (i.e., not related to X or Y), then this leads to:
An increase in the variance of the OLS estimator
answer: 4:
Consider the linear probability model Yi = β0 + β1Xi + ui. Assume E(ui|Xi)=0. the following statements are true:
The predicted value of the dependent variable will always be between 0 and 1. Thus, the OLS estimator of β1 is unbiased
Add Answer to:
Question 3
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