Consider the following generic fixed effects model, along with a two-period (t = 1, 2), randomly sampled panel data set with dependent variable y and independent variable x: Yit = Bo + 5o d24 + B1rit + ai +Uit where Yit = value of y for individual i at timet d2; = binary variable equal to 1 in the second time period (t = 2), and o otherwise (t = 1) Cit = value of a for individual i at time t a; = unobserved (time-invariant) effect Uj = idiosyncratic error Taking the first difference of the model (that is, subtracting the regression equation for t=1 from the regression equation for t=2) yields the following first-differenced equation: Note: Let A denote the change from t=1 to t = 2. Ay; = Yi2 – Yi1= So Au; You now plan to use OLS to estimate your first-differenced equation, in order to obtain the first-differenced estimator. Suppose that ait is a binary variable equal to 1 if the individual was born male, and o otherwise. True or False: The first-differenced estimator cannot be estimated in this case. O True O False

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Consider the following generic fixed effects model, along with a two-period (t = 1, 2), randomly sampled panel data set with dependent variable y and
independent variable x:
Yit = Bo + 5o d24 + B1rit + ai +Uit
where
Yit = value of y for individual i at timet
d2; = binary variable equal to 1 in the second time period (t = 2), and o otherwise (t = 1)
Cit = value of a for individual i at time t
a; = unobserved (time-invariant) effect
Uj = idiosyncratic error
Taking the first difference of the model (that is, subtracting the regression equation for t=1 from the regression equation for t=2) yields the following
first-differenced equation:
Note: Let A denote the change from t=1 to t = 2.
Ay; = Yi2 – Yi1=
So
Au;
You now plan to use OLS to estimate your first-differenced equation, in order to obtain the first-differenced estimator. Suppose that ait is a binary
variable equal to 1 if the individual was born male, and o otherwise.
True or False: The first-differenced estimator cannot be estimated in this case.
O True
O False
Transcribed Image Text:Consider the following generic fixed effects model, along with a two-period (t = 1, 2), randomly sampled panel data set with dependent variable y and independent variable x: Yit = Bo + 5o d24 + B1rit + ai +Uit where Yit = value of y for individual i at timet d2; = binary variable equal to 1 in the second time period (t = 2), and o otherwise (t = 1) Cit = value of a for individual i at time t a; = unobserved (time-invariant) effect Uj = idiosyncratic error Taking the first difference of the model (that is, subtracting the regression equation for t=1 from the regression equation for t=2) yields the following first-differenced equation: Note: Let A denote the change from t=1 to t = 2. Ay; = Yi2 – Yi1= So Au; You now plan to use OLS to estimate your first-differenced equation, in order to obtain the first-differenced estimator. Suppose that ait is a binary variable equal to 1 if the individual was born male, and o otherwise. True or False: The first-differenced estimator cannot be estimated in this case. O True O False
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