# BUSS-G350

The Fixed Effects regression model:

A. the slope coefficients are allowed to differ across entities, but the intercept is "fixed" (remains unchanged).

B. has n different intercepts.

C. eliminates the effect of heteroskedasticity.

D. in a log-log model may include logs of the binary variables, which control for the fixed effects.
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The Fixed Effects regression model:

A. the slope coefficients are allowed to differ across entities, but the intercept is "fixed" (remains unchanged).

B. has n different intercepts.

C. eliminates the effect of heteroskedasticity.

D. in a log-log model may include logs of the binary variables, which control for the fixed effects.
In the Fixed Effects regression model, you should exclude one of the binary variables for the entities when an intercept is present in the equation:

A. to allow for some changes between entities to take place.

B. because one of the entities is always excluded.

C. because there are already too many coefficients to estimate.

D. to avoid perfect multicollinearity.
In the Fixed Effects regression model, using (n - 1) binary variables for the entities, the coefficient of the binary variable indicates:

A. the difference in fixed effects between the ith and the first entity.

B. the level of the fixed effect of the ith entity.

C. will be either 0 or 1.

D. the response in the dependent variable to a percentage change in the binary variable.
If you included both time and entity fixed effects in the regression model which includes a constant, then:

A. you must exclude one of the entity binary variables and one of the time binary variables for the OLS estimator to exist.

B. one of the explanatory variables needs to be excluded to avoid perfect multicollinearity.

C. you can use the "before and after" specification even for T > 2.

D. the OLS estimator no longer exists.
Consider estimating the effect of the beer tax on the fatality rate, using time and state fixed effect for the Northeast Region of the United States (Maine, Vermont, New Hampshire, Massachusetts, Connecticut and Rhode Island) for the period 1991-2001. If Beer Tax was the only explanatory variable, how many coefficients would you need to estimate, excluding the constant?

A. 11

B. 17

C. 18

D. 7
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 Fq,∞ distribution, where q equals:

A. 53

B. 7

C. 58

D. 6
The main advantage of using panel data over cross sectional data is that it:

A. gives you more observations.

B. allows you to control for some types of omitted variables without actually observing them.

C. allows you to analyze behavior across time but not across entities.

D. allows you to look up critical values in the standard normal distribution.
Time Fixed Effects regression are useful in dealing with omitted variables:

A. if these omitted variables are constant across entities but vary over time.

B. when there are more than 100 observations.

C. if these omitted variables are constant across entities but not over time.

D. gives you more observations.
When you add state fixed effects to a simple regression model for U.S. states over a certain time period, and the regression R2 increases significantly, then it is safe to assume that:

A. state fixed effects account for a large amount of the variation in the data.

B. the included explanatory variables, other than the state fixed effects, are unimportant.

C. the coefficients on the other included explanatory variables will not change.

D. time fixed effects are unimportant