CreditCard | R Documentation |

Cross-section data on the credit history for a sample of applicants for a type of credit card.

`data("CreditCard")`

A data frame containing 1,319 observations on 12 variables.

- card
Factor. Was the application for a credit card accepted?

- reports
Number of major derogatory reports.

- age
Age in years plus twelfths of a year.

- income
Yearly income (in USD 10,000).

- share
Ratio of monthly credit card expenditure to yearly income.

- expenditure
Average monthly credit card expenditure.

- owner
Factor. Does the individual own their home?

- selfemp
Factor. Is the individual self-employed?

- dependents
Number of dependents.

- months
Months living at current address.

- majorcards
Number of major credit cards held.

- active
Number of active credit accounts.

According to Greene (2003, p. 952) `dependents`

equals `1 + number of dependents`

,
our calculations suggest that it equals `number of dependents`

.

Greene (2003) provides this data set twice in Table F21.4 and F9.1, respectively.
Table F9.1 has just the observations, rounded to two digits. Here, we give the
F21.4 version, see the examples for the F9.1 version. Note that `age`

has some
suspiciously low values (below one year) for some applicants. One of these differs
between the F9.1 and F21.4 version.

Online complements to Greene (2003). Table F21.4.

https://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm

Greene, W.H. (2003). *Econometric Analysis*, 5th edition. Upper Saddle River, NJ: Prentice Hall.

`Greene2003`

```
data("CreditCard")
## Greene (2003)
## extract data set F9.1
ccard <- CreditCard[1:100,]
ccard$income <- round(ccard$income, digits = 2)
ccard$expenditure <- round(ccard$expenditure, digits = 2)
ccard$age <- round(ccard$age + .01)
## suspicious:
CreditCard$age[CreditCard$age < 1]
## the first of these is also in TableF9.1 with 36 instead of 0.5:
ccard$age[79] <- 36
## Example 11.1
ccard <- ccard[order(ccard$income),]
ccard0 <- subset(ccard, expenditure > 0)
cc_ols <- lm(expenditure ~ age + owner + income + I(income^2), data = ccard0)
## Figure 11.1
plot(residuals(cc_ols) ~ income, data = ccard0, pch = 19)
## Table 11.1
mean(ccard$age)
prop.table(table(ccard$owner))
mean(ccard$income)
summary(cc_ols)
sqrt(diag(vcovHC(cc_ols, type = "HC0")))
sqrt(diag(vcovHC(cc_ols, type = "HC2")))
sqrt(diag(vcovHC(cc_ols, type = "HC1")))
bptest(cc_ols, ~ (age + income + I(income^2) + owner)^2 + I(age^2) + I(income^4), data = ccard0)
gqtest(cc_ols)
bptest(cc_ols, ~ income + I(income^2), data = ccard0, studentize = FALSE)
bptest(cc_ols, ~ income + I(income^2), data = ccard0)
## More examples can be found in:
## help("Greene2003")
```