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Question 1

Difference between Probit and Logit

Answer 1

• Economists prefer Probit because by assuming error term is standard normal distribution, findings can be generalized easily
• In industry/corporate and health, Logit is preferred
• when the error term has a logistic distribution, logit is better. When the error term has a standard normal distribution, probit is better.

Question 2

Method of Maximum Likelihood Estimation

Answer 2

finds the coefficients of the independent variables that maximize prediction of the dependent variable; produces MLEs (coefficients)

Question 3

Logit parameter interpretation methods

Answer 3

• Odds ratios
• Log odds
• marginal effects

Question 4

Odds Ratio

Answer 4

P/(1-P); odds of success divided by odds of failure; not the same as PROBABILITY of success.

Question 5

Odds Ratio Method

Answer 5

find e^B; e^B is your multiplicative factor of change. To find percent change: (e^B - 1)*100.

Question 6

Odds Ratio Method Interpretation: age predicts divorce rate, coefficient = 3.12

Answer 6

For every additional unit of age, odds of divorce (success) changes by a multiplicative factor of 22.65, ceteris paribus.

Question 7

Log Odds

Answer 7

log(odds); makes interpretation odds less than and greater than one.

Question 8

Log Odds Method

Answer 8

no operation, coefficients translate directly to interpretation.

Question 9

Log Odds Method Interpretation: age predicts divorce rate, coefficient = 3.12

Answer 9

For every additional unit of age, the log odds of divorce (success) change by a multiplicative factor of 3.12.

Question 10

Marginal Effect Method

Answer 10

1. Find mean values of all independent variables; categorical variables have a mean value of [percent frequency of class / 100 ]
2. Find probability of success at the mean values: M = e^(B0 + B1X1mean + B2X2mean …); P = M/(1+M)
3. plug coefficient into equation to find “Marginal Effect at the Mean” for a specific variable: MEM = (B * P) * (1 - P)
4. (Optional) multiply the MEM * 100 to find its percent effect on the probability of success.

Question 11

Marginal Effect Method Interpretation; age predicts divorce rate, MEM coefficient = 0.256

Answer 11

For every additional unit of age, the probability of divorce (success) increases by a multiplicative factor of 0.256, ceteris paribus.

Question 12

Probit parameter interpretation methods

Answer 12

• z-score
• marginal effect

Question 13

z-score method

Answer 13

no operation; coefficients used directly.

Question 14

z-score method interpretation: age predicts divorce rate, coefficient = 3.12

Answer 14

For every additional unit of age, the z-score of divorce (success) increases by a factor of 3.12, ceteris paribus.

Question 15

Marginal Effect Method

Answer 15

1. Z = B0 + B1X1 + B2X2… Find Z at the mean by plugging average from each independent variable.
2. Plug Z into the nasty phi equation. o(Z) = (1/root(2pie))e^-(Z^2/2)
3. o(Z) * B; just o(Z) times the coefficient.

Question 16

Marginal Effect Method Interpretation: age predicts divorce rate, MEM divorce rate = 0.112

Answer 16

For every additional of age, the probability of divorce (success) changes by a multiplicative factor of 0.112, ceteris paribus.

Question 17

Linear Regression for binary categorical dependent variables

Answer 17

can be used when avg. success probability is between 3% and 98%. The middle of the curve is close enough to straight. Otherwise, the main problem is that its predictions for certain values might not make sense.