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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B. has n different intercepts.

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.

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.

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

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.

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

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

B. 17

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

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.

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

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.

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

In panel data, the regression error:

A. should be calculated taking into account heteroskedasticity but not autocorrelation.

B. is likely to be correlated over time within an entity.

C. only exists for the case of T > 2.

D. fits all of the three descriptions above.

B. is likely to be correlated over time within an entity.

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

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

The binary dependent variable model is an example of a: A. regression model, which has as a regressor, among others, a binary variable. B. model that cannot be estimated by OLS. C. limited dependent variable model. D. model where the left-hand variable is measured in base 2.C. limited dependent variable model.In the binary dependent variable model, a predicted value of 0.6 means that: A. the most likely value the dependent variable will take on is 60 percent. B. the model makes little sense, since the dependent variable can only be 0 or 1. C. given the values for the explanatory variables, there is a 60 percent probability that the dependent variable will equal one. D. given the values for the explanatory variables, there is a 40 percent probability that the dependent variable will equal one.C. given the values for the explanatory variables, there is a 60 percent probability that the dependent variable will equal one.E(Y|X1,..., Xk) = Pr(Y = 1| X1,..., Xk) means that: A. dividing Y by the X's is the same as the probability of Y being the inverse of the sum of the X's. B. the exponential of Y is the same as the probability of Y happening. C. you are pretty certain that Y takes on a value of 1 given the X's. D. for a binary variable model, the predicted value from the population regression is the probability that Y=1, given X.D. for a binary variable model, the predicted value from the population regression is the probability that Y=1, given X.The linear probability model is: A. the application of the multiple regression model with a continuous left-hand side variable and a binary variable as at least one of the regressors. B. an example of probit estimation. C. another word for logit estimation. D. the application of the linear multiple regression model to a binary dependent variable.D. the application of the linear multiple regression model to a binary dependent variable.In the linear probability model, the interpretation of the slope coefficient is: A. not all that meaningful since the dependent variable is either 0 or 1. B. the response in the dependent variable to a percentage change in the regressor. C. the change in probability that Y=1 associated with a unit change in X, holding others regressors constant. D. the change in odds associated with a unit change in X, holding other regressors constant.C. the change in probability that Y=1 associated with a unit change in X, holding others regressors constant.The following tools from multiple regression analysis carry over in a meaningful manner to the linear probability model, with the exception of the: A. regression R2. B. 95% confidence interval using ± 1.96 times the standard error. C. significance test using the t-statistic. D. F-statistic.A. regression R2.The major flaw of the linear probability model is that: A. the predicted values can lie above 1 and below 0. B. people do not always make clear-cut decisions. C. the regression R2 cannot be used as a measure of fit. D. the actuals can only be 0 and 1, but the predicted are almost always different from that.A. the predicted values can lie above 1 and below 0.An alternative method of estimating Binary Outcome Models is the Logit Model. A. True B. FalseTrueThe following are reasons for studying randomized controlled experiment in an econometrics course, with the exception of: A. at a conceptual level, the notion of an ideal randomized controlled experiment provides a benchmark against which to judge estimates of causal effects in practice. B. when experiments are actually conducted, their results can be very influential, so it is important to understand the limitations and threats to validity of actual experiments as well as their strength. C. randomized controlled experiments in economics are common. D. external circumstances sometimes produce what appears to be randomization.C. randomized controlled experiments in economics are common.In the context of a controlled experiment, consider the simple linear regression formulation Yi = β0 + β1Xi + ui. Let the Yi be the outcome, Xi the treatment level, and ui contain all the additional determinants of the outcome. Then: A. β0 represents the causal effect of X on Y when X is zero. B. E(Y| X = 0)is the expected value for the treatment group. C. Xi and ui will be independently distributed if the Xi be are randomly assigned. D. the OLS estimator of the slope will be inconsistent in the case of a randomly assigned Xi since there are omitted variables presentC. Xi and ui will be independently distributed if the Xi be are randomly assigned.In the context of a controlled experiment, consider the simple linear regression formulation Yi = β0 + β1Xi + ui. Let the Yi be the outcome, Xi the treatment level when the treatment is binary, and ui contain all the additional determinants of the outcome. Then calling a differences estimator: A. makes sense since it is the difference between the sample average outcome of the treatment group and the sample average outcome of the control group. B. and the level estimator is standard terminology in randomized controlled experiments. C. does not make sense, since neither Y nor X are in differences. D. is not quite accurate since it is actually the derivative of Y on X.A. makes sense since it is the difference between the sample average outcome of the treatment group and the sample average outcome of the control group.The following does not represent a threat to internal validity of randomized controlled experiments: A. a large sample size. B. attrition. C. failure to follow the treatment protocol. D. experimental effects.A. a large sample size.The following is not a threat to external validity: A. experimental participants are volunteers. B. the treatment being studied is not representative of the treatment that would be implemented more broadly. C. the experimental sample is not representative of the population of interest. D. partial compliance with the treatment protocol.D. partial compliance with the treatment protocol.Assume that data are available on other characteristics of the subjects that are relevant to determining the experimental outcome. Then including these determinants explicitly results in: A. the differences in means test. B. large scale equilibrium effects. C. the multiple regression model. D. the limited dependent variable model.C. the multiple regression model.Experimental data are often: A. observational data B. time series data C. binary data, in that the subject either does or does not respond to the treatment. D. panel dataD. panel data