Home | Trắc nghiệm | Kinh tế lượng | 180 câu trắc nghiệm Kinh tế lượng – Phần 4
Tổng hợp 180 câu trắc nghiệm Kinh tế lượng cơ bản trong tài chính bằng tiếng anh (có đáp án kèm theo)
Tổng hợp 180 câu trắc nghiệm Kinh tế lượng cơ bản trong tài chính bằng tiếng anh (có đáp án kèm theo)

180 câu trắc nghiệm Kinh tế lượng – Phần 4

Chapter 10: Regression with Panel Data

KTL_001_C10_1: The Fixed Effects regression model
● has n different intercepts.
○ the slope coefficients are allowed to differ across entities, but the intercept is “fixed” (remains unchanged).
○ has “fixed” (repaired) the effect of heteroskedasticity.
○ in a log-log model may include logs of the binary variables, which control for the fixed effects.

KTL_001_C10_2: 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
○ because the first time period must always excluded from your data set.
○ because there are already too many coefficients to estimate.
● to avoid perfect multicollinearity.
○ to allow for some changes between time periods to take place.

KTL_001_C10_3: When you add state fixed effects to a simple regression model for U.S. states over a certain time period, and the regression \({R^2}\) increases significantly, then it is safe to assume that
○ the included explanatory variables, other than the state fixed effects, are unimportant.
● state fixed effects account for a large amount of the variation in the data.
○ the coefficients on the other included explanatory variables will not change.
○ time fixed effects are unimportant.

KTL_001_C10_4: In the panel regression analysis of beer taxes on traffic deaths, the estimation period is 1982-1988 for the 48 contiguous U.S. states. To test for the significance of entity fixed effects, you should calculate the F-statistic and compare it to the critical value from your \({F_{q,\infty }}\) distribution, where q equals
○ 48.
○ 54.
○ 7.
● 47.

KTL_001_C10_5: In the panel regression analysis of beer taxes on traffic deaths, the estimation period is 1982-1988 for the 48 contiguous U.S. states. To test for the significance of time fixed effects, you should calculate the F-statistic and compare it to the critical value from your \({F_{q,\infty }}\) distribution, which equals (at the 5% level)
○ 2.01.
● 2.10.
○ 2.80.
○ 2.64.

KTL_001_C10_6: Assume that for the T = 2 time periods case, you have estimated a simple regression in changes model and found a statistically significant positive intercept. This implies
○ a negative mean change in the LHS variable in the absence of a change in the RHS variable since you subtract the earlier period from the later period
○ that the panel estimation approach is flawed since differencing the data eliminates the constant (intercept) in a regression
● a positive mean change in the LHS variable in the absence of a change in the RHS variable
○ that the RHS variable changed between the two subperiods

KTL_001_C10_7: HAC standard errors and clustered standard errors are related as follows:
○ they are the same
● clustered standard errors are one type of HAC standard error
○ they are the same if the data is differenced
○ clustered standard errors are the square root of HAC standard errors

KTL_001_C10_8: In panel data, the regression error
● is likely to be correlated over time within an entity
○ should be calculated taking into account heteroskedasticity but not autocorrelation
○ only exists for the case of T > 2
○ fits all of the three descriptions above

KTL_001_C10_9: It is advisable to use clustered standard errors in panel regressions because
○ without clustered standard errors, the OLS estimator is biased
○ hypothesis testing can proceed in a standard way even if there are few entities (n is small)
○ they are easier to calculate than homoskedasticity-only standard errors
● the fixed effects estimator is asymptotically normally distributed when n is large

KTL_001_C10_10: If Xit is correlated with Xis for different values of s and t, then
● Xit is said to be autocorrelated
○ the OLS estimator cannot be computed
○ statistical inference cannot proceed in a standard way even if clustered standard errors are used
○ this is not of practical importance since these correlations are typically weak in applications