Ch. 11 - econometrics

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

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) given the values for the explanatory variables, there is a 60 percent probability that the dependent variable will equal one.

C) the model makes little sense, since the dependent variable can only be 0 or 1.

D) given the values for the explanatory variables, there is a 40 percent probability that the dependent variable will equal one.

B

E(Y X1, ..., Xk) = Pr(Y = 1 X1,..., Xk) means that

A) for a binary variable model, the predicted value from the population regression is the probability that Y=1, given X.

B) dividing Y by the X's is the same as the probability of Y being the inverse of the sum of the X's.

C) the exponential of Y is the same as the probability of Y happening.

D) you are pretty certain that Y takes on a value of 1 given the X's.

A

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

In the linear probability model, the interpretation of the slope coefficient is

A) the change in odds associated with a unit change in X, holding other regressors constant.

B) not all that meaningful since the dependent variable is either 0 or 1.

C) the change in probability that Y=1 associated with a unit change in X, holding others regressors constant.

D) the response in the dependent variable to a percentage change in the regressor.

C

The following tools from multiple regression analysis carry over in a meaningful manner to the linear probability model, with the exception of the

A) F-statistic.

B) significance test using the t-statistic.

C) 95% confidence interval using ± 1.96 times the standard error.

D) regression R2.

D

The major flaw of the linear probability model is that

A) the actuals can only be 0 and 1, but the predicted are almost always different from that.

B) the regression R2 cannot be used as a measure of fit.

C) people do not always make clear-cut decisions.

D) the predicted values can lie above 1 and below 0.

D

The probit model

A) is the same as the logit model.

B) always gives the same fit for the predicted values as the linear probability model for values between 0.1 and 0.9.

C) forces the predicted values to lie between 0 and 1.

D) should not be used since it is too complicated.

C

The logit model derives its name from

A) the logarithmic model.

B) the probit model.

C) the logistic function.

D) the tobit model.

C

In the probit model Pr(Y = 1 = Φ(β0 + β1X), Φ

A) is not defined for Φ(0).

B) is the standard normal cumulative distribution function.

C) is set to 1.96.

D) can be computed from the standard normal density function.

B

In the expression Pr(Y = 1 = Φ(β0 + β1X),

A) (β0 + β1X) plays the role of z in the cumulative standard normal distribution function.

B) β1 cannot be negative since probabilities have to lie between 0 and 1.

C) β0 cannot be negative since probabilities have to lie between 0 and 1.

D) min (β0 + β1X) > 0 since probabilities have to lie between 0 and 1.

A

In the probit model Pr(Y = 1 X1, X2,..., Xk) = Φ(β0 + β1X1 + βxX2 + ... + βkXk),

A) the β's do not have a simple interpretation.

B) the slopes tell you the effect of a unit increase in X on the probability of Y.

C) β0 cannot be negative since probabilities have to lie between 0 and 1.

D) β0 is the probability of observing Y when all X's are 0

A

The maximum likelihood estimation method produces, in general, all of the following desirable properties with the exception of

A) efficiency.

B) consistency.

C) normally distributed estimators in large samples.

D) unbiasedness in small samples.

D

When having a choice of which estimator to use with a binary dependent variable, use

A) probit or logit depending on which method is easiest to use in the software package at hand.

B) probit for extreme values of X and the linear probability model for values in between.

C) OLS (linear probability model) since it is easier to interpret.

D) the estimation method which results in estimates closest to your prior expectations.

A

When estimating probit and logit models,

A) the t-statistic should still be used for testing a single restriction.

B) you cannot have binary variables as explanatory variables as well.

C) F-statistics should not be used, since the models are nonlinear.

D) it is no longer true that the 2 < R2.

A

Probit coefficients are typically estimated using

A) the OLS method

B) the method of maximum likelihood

C) non-linear least squares (NLLS)

D) by transforming the estimates from the linear probability model

B

F-statistics computed using maximum likelihood estimators

A) cannot be used to test joint hypothesis

B) are not meaningful since the entire regression R2 concept is hard to apply in this situation

C) do not follow the standard F distribution

D) can be used to test joint hypothesis

D