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What are the assumptions A1-A4 for the linear model Y = X\beta + U?
What are the assumptions A1-A4 for the linear model Y = X\beta + U?
The assumptions A1-A4 for the linear model Y = X\beta + U are: 1) mean independent error E[U |X] = 0, 2) no multicollinearity/full rank of E[XX^T], 3) random (iid) sampling, and 4) conditionally homoskedastic error.
If the error U satisfies V ar [U |X] = \gamma^T X, where \gamma is a vector of constants, is OLS the best linear unbiased estimator (BLUE)?
If the error U satisfies V ar [U |X] = \gamma^T X, where \gamma is a vector of constants, is OLS the best linear unbiased estimator (BLUE)?
Yes, if the error U satisfies V ar [U |X] = \gamma^T X, where \gamma is a vector of constants, then OLS is the best linear unbiased estimator (BLUE).
Why are the usual OLS based standard errors incorrect when the error is conditionally homoskedastic?
Why are the usual OLS based standard errors incorrect when the error is conditionally homoskedastic?
The usual OLS based standard errors are incorrect when the error is conditionally homoskedastic because the variance estimator is inconsistent.
What are the consequences of guessing on the True or False section of the exam?
What are the consequences of guessing on the True or False section of the exam?
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What is the maximum number of credits that can be earned on the True or False section of the exam?
What is the maximum number of credits that can be earned on the True or False section of the exam?
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