Home | Trắc nghiệm | Kinh tế lượng | 180 câu trắc nghiệm Kinh tế lượng – Phần 4

180 câu trắc nghiệm Kinh tế lượng – Phần 4

Chapter 11: Regression with a Binary Dependent Variable

KTL_001_C11_1: The linear probability model is
○ 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.
○ an example of probit estimation.
○ another word for logit estimation.
● the application of the linear multiple regression model to a binary dependent variable.

KTL_001_C11_2: The probit model
○ is the same as the logit model.
○ always gives the same fit for the predicted values as the linear probability model for values between 0.1 and 0.9.
● forces the predicted values to lie between 0 and 1.
○ should not be used since it is too complicated.

KTL_001_C11_3: In the expression Pr(deny = 1| P/I Ratio, black) = F(–2.26 + 2.74P/I ratio + 0.71black), the effect of increasing the P/I ratio from 0.3 to 0.4 for a white person
○ is 0.274 percentage points.
● is 6.1 percentage points.
○ should not be interpreted without knowledge of the regression \({R^2}\).
○ is 2.74 percentage points.

KTL_001_C11_4: Nonlinear least squares
● solves the minimization of the sum of squared predictive mistakes through sophisticated mathematical routines, essentially by trial and error methods.
○ should always be used when you have nonlinear equations.
○ gives you the same results as maximum likelihood estimation.
○ is another name for sophisticated least squares.

KTL_001_C11_5: To measure the fit of the probit model, you should:
○ use the regression \({R^2}\).
○ plot the predicted values and see how closely they match the actuals.
○ use the log of the likelihood function and compare it to the value of the likelihood function.
● use the fraction correctly predicted or the pseudo \({R^2}\).

KTL_001_C11_6: In the probit regression, the coefficient \({\beta _1}\) indicates
○ the change in the probability of Y = 1 given a unit change in X
○ the change in the probability of Y = 1 given a percent change in X
● the change in the z- value associated with a unit change in X
○ none of the above

KTL_001_C11_7: Your textbook plots the estimated regression function produced by the probit regression of deny on P/I ratio. The estimated probit regression function has a stretched “S” shape given that the coefficient on the P/I ratio is positive. Consider a probit regression function with a negative coefficient. The shape would
● resemble an inverted “S” shape (for low values of X, the predicted probability of Y would approach 1)
○ not exist since probabilities cannot be negative
○ remain the “S” shape as with a positive slope coefficient
○ would have to be estimated with a logit function

KTL_001_C11_8: Probit coefficients are typically estimated using
○ the OLS method
● the method of maximum likelihood
○ non-linear least squares (NLLS)
○ by transforming the estimates from the linear probability model

KTL_001_C11_9: F-statistics computed using maximum likelihood estimators
○ cannot be used to test joint hypothesis
○ are not meaningful since the entire regression \({R^2}\) concept is hard to apply in this situation
○ do not follow the standard F distribution
● can be used to test joint hypothesis

KTL_001_C11_10: When testing joint hypothesis, you can use
○ the F- statistic
○ the chi-squared statistic
● either the F-statistic or the chi-square statistic
○ none of the above