---
title: "Estimating the Tobit-1 model with the charitable data set"
date: today
date-format: long
bibliography: ../inst/REFERENCES.bib
vignette: >
  %\VignetteIndexEntry{Estimating the Tobit-1 model with the charitable data set}
  %\VignetteEngine{quarto::pdf}
  %\VignetteEncoding{UTF-8}
---

We'll reproduce here some results obtained by @WILH:08 using a data
set which deals with charitable giving. The `charitable` data set is
shipped with the **micsr** package.

```{r}
library(micsr)
```

```{r}
head(charitable, 5)
```

The response is called `donation`, it measures annual charitable
givings in $US. This variable is left-censored for the value of 25, as
this value corresponds to the item "less than 25 $US
donation". Therefore, for this value, we have households who didn't
make any charitable giving and some which made a small giving (from 1
to 24 $US).

The covariates used are the donation made by the parents
(`donparents`), two factors indicating the educational level and
religious beliefs (respectively `education` and `religion`), annual
income (`income`) and two dummies for living in the south (`south`)
and for married couples (`married`). 

@WILH:08 considers the value of the donation in logs and subtract $\ln
25$, so that the response is 0 for households who gave no donation or
a small donation.


```{r}
charitable$logdon <- with(charitable, log(donation) - log(25))
```

The tobit model can be estimated by maximum likelihood using
`AER::tobit`, `censReg::censReg` or with the `tobit1` package. 

```{r}
#| message: false
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)
```

**tobit1** provides a rich set of estimation methods, especially the
trimmed or **SCLS** (symmetrically censored least squares) estimator
proposed by @POWE:86. We also, for pedagogical purposes, estimate the
OLS estimator although it is known to be inconsistent.


```{r }
scls <- update(ml, method = "trimmed")
ols <- update(ml, method = "lm")
```

The results of the three models are presented in table 1:

```{r}
#| label: tbl-models
#| echo: false
#| tbl-cap: "Estimation of charitable giving models"
#| message: false
#| eval: false
#| include: false
if (requireNamespace("modelsummary")){
    modelsummary::msummary(list("OLS" = ols, "maximum likehihood" = ml, "SCLS" = scls),
                           single.row = TRUE, digits = 3)
}
```

The results match exactly the first two columns of [@WILH:08, table 3
page 577].

Note that the OLS estimators are all lower in absolute values than
those of the two other estimators, which illustrate the fact that OLS
estimators are biased toward zero when the response is censored. The
maximum likelihood is consistent and asymptotically efficient if the
conditional distribution of $y^*$ (the latent variable) is
homoskedastic and normal.

Specification tests for the maximum likelihood estimator can  be conducted
using conditional moments tests. This can easily be done using the
`micsr::cmtest` function, which can take as input a model fitted by
either `AER::tobit`, `censReg::censReg` or `tobit1::tobit1`:


```{r}
#| collapse: true
cmtest(ml) |> gaze()
```

```{r}
#| include: false
cmtest(ml_aer)
cmtest(ml_creg)
```

`cmtest` has a `test` argument with default value equal to
`normality`. To get a heteroskedasticity test, we would use:

```{r}
#| collapse: true
cmtest(ml, test = "heterosc") |> gaze()
```
Normality and heterosledasticity are strongly rejected. The values are
different from @WILH:08 as he used the "outer product of the gradient"
form of the test. These versions of the test can be obtained by
setting the `OPG` argument to `TRUE`.

```{r}
#| collapse: true
cmtest(ml, test = "normality", opg = TRUE) |> gaze()
cmtest(ml, test = "heterosc", opg = TRUE) |> gaze()
```

Non-normality can be further investigate by testing separately
the fact that the skewness and kurtosis indicators are respectively
different from 0 and 3.

```{r}
#| collapse: true
cmtest(ml, test = "skewness") |> gaze()
cmtest(ml, test = "kurtosis") |> gaze()
```

The hypothesis that the conditional distribution of the response is
mesokurtic is not rejected at the 1% level and the main problem seems
to be the asymmetry of the distribution, even after taking the
logarithm of the response. 

This can be illustrated (see @fig-histnorm) by plotting the
(unconditional) distribution of the response (for positive values) and
adding to the histogram the normal density curve.


```{r}
#| label: fig-histnorm
#| fig-cap: "Empirical distribution of the response and normal approximation"
#| echo: false
#| message: false
if (requireNamespace("ggplot2")){
    library(ggplot2)
    mu <- mean(subset(charitable, logdon > 0)$logdon)
    std <- sd(subset(charitable, logdon > 0)$logdon)
    ggplot(subset(charitable, logdon > 0), aes(logdon)) +
        geom_histogram(aes(y = after_stat(density)), color = "black",
                       fill = "white", bins = 10) +
        geom_function(fun = dnorm, args = list(mean = mu,
                                               sd = std)) +
        labs(x = "log of charitable giving", y = NULL)
}
```

# References
