Final

The Fixed Effects regression model:
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The Fixed Effects regression model:
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:
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:
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:
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?
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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:
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Time Fixed Effects regression are useful in dealing with omitted variables:
if these omitted variables are constant across entities but vary over time.
The main advantage of using panel data over cross sectional data is that it:
allows you to control for some types of omitted variables without actually observing them.
In panel data, the regression error:
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:
state fixed effects account for a large amount of the variation in the data.
The binary dependent variable model is an example of a:limited dependent variable model.In the binary dependent variable model, a predicted value of 0.6 means that: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: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: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: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:regression R2.The major flaw of the linear probability model is that:the predicted values can lie above 1 and below 0.An alternative method of estimating Binary Outcome Models is the Logit Model.TrueProvide an example of a Binary Outcome (Limited Dependent Variable).Will a person be approved for a mortgage or no.