Autoregrsssive distributed lag models include A] current and lagged values of the error term. B} current and lagged values of the residuals. C] lags of the dependent vrariable1 and lagged values of additional predictor variables. D] lags and leads of the dependent variable. The binary dependent variable model is an example of a A} regression model, which has binary independent variables. E]- model that cannot be estimated by 01.3. (3] limited dependent variable model. D} model where the lefthand variable is measured in base 2. The major problem of the linear probability model is that A} the predicted probabilities may be negative or larger than 1. B} the dependent variable is binary, yet the predicted values of this model are almost never exactly D or 1. C] the regression R2 cannot be used as a measure of t. D} people do not always make clear-cut decisions. The following problems could be analysed using logistic estimation with the exception of A) whether or not a college student decides to study abroad for one semester. E]- whether or not being a female has an effect on earnings. (3] whether or not an applicant will be accepted to some program. D} whether or not a student can pass a qualication exam. In the linear probability model1 the interpretation of the slope coeieient is A) the change in odds associated with a unit change in I, holding other independent variables constant. B} not meaningful since the dependent variable is either I] or 1. C] the change in probability that Y=1 a-Eociated with a unit change in I. holding others regressors constant. D} the response in the dependent variable to a percentage change in the regressor