8) The relationship between the TSLS slope and the corresponding population parameter is: (Z; - Zju; i=1 A) (§ 7SLS - B1) = E (Zi - ZX; - X) i=1 £ (Z; - Z) i=1 B) (§ ȚSLS - B1) = £ (Zi - Z(X¡ - X) i=1 1 TSLS i=1 E (Zi - Z2 i=1 E (X; - Xju; TSLS i=1 { (Z; - ZX; - X) i=1

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8) The relationship between the TSLS slope and the corresponding population parameter is:
(Z; - Zju;
i=1
A) (§ 7SLS - B1) =
E (Zi - ZX; - X)
i=1
£ (Z; - Z)
i=1
B) (§ ȚSLS - B1) =
£ (Zi - Z(X¡ - X)
i=1
1
TSLS
i=1
E (Zi - Z2
i=1
(X; - Xju;
i=1
D) (§ SLS - B1) =
- E (Z; - ZxX¡ - X)
i=1
9) In the Fixed Effects regression model, using (n – 1) binary variables for the entities, the
coefficient of the binary variable indicates:
A) the level of the fixed effect of the ith entity.
B) will be either 0 or 1.
C) the difference in fixed effects between the ith and the first entity.
D) the response in the dependent variable to a percentage change in the binary variable.
10) In the Fixed Time Effects regression model, you should exclude one of the binary variables
for the time periods when an intercept is present in the equation:
A) because the first time period must always excluded from your data set.
B) because there are already too many coefficients to estimate.
C) to avoid perfect multicollinearity.
D) to allow for some changes between time periods to take place.
Transcribed Image Text:8) The relationship between the TSLS slope and the corresponding population parameter is: (Z; - Zju; i=1 A) (§ 7SLS - B1) = E (Zi - ZX; - X) i=1 £ (Z; - Z) i=1 B) (§ ȚSLS - B1) = £ (Zi - Z(X¡ - X) i=1 1 TSLS i=1 E (Zi - Z2 i=1 (X; - Xju; i=1 D) (§ SLS - B1) = - E (Z; - ZxX¡ - X) i=1 9) In the Fixed Effects regression model, using (n – 1) binary variables for the entities, the coefficient of the binary variable indicates: A) the level of the fixed effect of the ith entity. B) will be either 0 or 1. C) the difference in fixed effects between the ith and the first entity. D) the response in the dependent variable to a percentage change in the binary variable. 10) In the Fixed Time Effects regression model, you should exclude one of the binary variables for the time periods when an intercept is present in the equation: A) because the first time period must always excluded from your data set. B) because there are already too many coefficients to estimate. C) to avoid perfect multicollinearity. D) to allow for some changes between time periods to take place.
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