charitable.R
In micsr: Microeconometrics with R

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(message = FALSE, warning = FALSE, out.width = "100%",
                      fig.align = "center", cache = FALSE, echo = TRUE)
options(knitr.table.format = "latex", knitr.booktabs = TRUE)

## -----------------------------------------------------------------------------
library("micsr")

## -----------------------------------------------------------------------------
print(charitable, n = 5)

## -----------------------------------------------------------------------------
charitable$logdon <- with(charitable, log(donation) - log(25))

## -----------------------------------------------------------------------------
char_form <- logdon ~ log(donparents) + log(income) +
    education + religion + married + south
if (requireNamespace("AER")){
    library("AER")
    ml_aer <- tobit(char_form, data = charitable)
}
if (requireNamespace("censReg")){
    library("censReg")
    ml_creg <- censReg(char_form, data = charitable)
}
ml <- tobit1(char_form, data = charitable)

## -----------------------------------------------------------------------------
scls <- update(ml, method = "trimmed")
ols <- update(ml, method = "lm")

## ----models, echo = FALSE-----------------------------------------------------
if (requireNamespace("modelsummary")){
    modelsummary::msummary(list("OLS" = ols, "maximum likehihood" = ml, "SCLS" = scls),
                           title = "Estimation of charitable giving models",
                           label = "tab:models", single.row = TRUE, digits = 3)
}

## -----------------------------------------------------------------------------
cmtest(ml)

## ----include = FALSE----------------------------------------------------------
cmtest(ml_aer)
cmtest(ml_creg)

## -----------------------------------------------------------------------------
cmtest(ml, test = "heterosc")

## -----------------------------------------------------------------------------
cmtest(ml, test = "normality", opg = TRUE)
cmtest(ml, test = "heterosc", opg = TRUE)

## -----------------------------------------------------------------------------
cmtest(ml, test = "skewness")
cmtest(ml, test = "kurtosis")

## ----histnorm, fig.cap = "Empirical distribution of the response and normal approximation", echo = FALSE----
library("ggplot2")
library("dplyr")
moments <- charitable %>% filter(logdon > 0) %>%
    summarise(mu = mean(logdon), sigma = sd(logdon))
ggplot(filter(charitable, logdon > 0), aes(logdon)) +
    geom_histogram(aes(y = ..density..), color = "black",
                   fill = "white", bins = 10) +
    geom_function(fun = dnorm, args = list(mean = moments$mu,
                                           sd = moments$sigma)) +
    labs(x = "log of charitable giving", y = NULL)