micsr (version 0.1-1)

tobit1: Truncated response model

Description

Estimation of models for which the response is truncated, either on censored or truncated samples using OLS, NLS, maximum likelihood, two-steps estimators or trimmed estimators

Usage

tobit1(
  formula,
  data,
  subset = NULL,
  weights = NULL,
  start = NULL,
  left = 0,
  right = Inf,
  scedas = NULL,
  sample = c("censored", "truncated"),
  method = c("ml", "lm", "twosteps", "trimmed", "nls", "minchisq", "test"),
  trace = FALSE,
  ...
)

Value

An object of class c("tobit1", "micsr"), see micsr::micsr for further details.

Arguments

formula

a symbolic description of the model; if two right hand sides are provided, the second one described the set of instruments if scedas is NULL, which is the default. Otherwise, the second part indicates the set of covariates for the variance function

data, subset, weights

see lm

start

an optional vector of starting values

left, right

left and right truncation points for the response The default is respectively 0 and +Inf which corresponds to the most classic (left-zero truncated) tobit model

scedas

the functional form used to specify the conditional variance, either "exp" or "pnorm"

sample

either "censored" (the default) to estimate the censored (tobit) regression model or "truncated" to estimated the truncated regression model

method

one of "ml" for maximum likelihood, "lm" for (biased) least squares estimators, "twosteps" for two-steps consistent estimators, "trimmed" for symetrically censored estimator, "minchisq" and "test". The last two are only relevant for instrumental variable estimation (when the formula is a two-parts formula and scedas is NULL)

trace

a boolean (the default if FALSE) if TRUE some information about the optimization process is printed

...

further arguments

Author

Yves Croissant

References

POWE:86micsr

Examples

charitable$logdon <- with(charitable, log(donation) - log(25))
ml <- tobit1(logdon ~ log(donparents) + log(income) + education +
             religion + married + south, data = charitable)
scls <- update(ml, method = "trimmed")
tr <- update(ml, sample = "truncated")
nls <- update(tr, method = "nls")