truncreg {truncreg}R Documentation

Models for truncated regressions

Description

Estimation of models with truncated explanatory variables by maximum likelihood

Usage

truncreg(formula, data, subset, weights, na.action,
       point = 0, direction = "left", ...)
## S3 method for class 'truncreg':
print(x, digits = max(3, getOption("digits") - 2),
    width = getOption("width"), ...)
## S3 method for class 'truncreg':
summary(object, ...)
## S3 method for class 'summary.truncreg':
print(x, digits = max(3, getOption("digits") - 2),
    width = getOption("width"), ...)
## S3 method for class 'truncreg':
logLik(object, ...)
## S3 method for class 'truncreg':
vcov(object, ...)
## S3 method for class 'truncreg':
residuals(object, ...)
## S3 method for class 'truncreg':
fitted(object, ...)

Arguments

x, object an object of class truncreg
formula a symbolic description of the model to be estimated,
data the data,
subset an optional vector specifying a subset of observations,
weights an optional vector of weights,
na.action a function which indicates what should happen when the data contains 'NA's,
point the value of truncation (the default is 0),
direction the direction of the truncation, either "left" (the default) or "right",
digits the number of digits,
width the width of the printing,
... further arguments.

Details

The model is estimated with the maxLik package and the Newton-Raphson method, using analytic gradient and hessian.

Value

An object of class "truncreg", a list with elements:

coefficients the named vector of coefficients,
vcov the variance matrix of the coefficients,
fitted.values the fitted values,
logLik the value of the log-likelihood,
gradient the gradient of the log-likelihood at convergence,
model the model frame used,
call the matched call,
est.stat some information about the estimation (time used, optimisation method),

Author(s)

Yves Croissant

References

Hausman, J.A. and D.A. Wise (1976) ``The evaluation of results from truncated samples: the New-Jersey negative invome tax experiment'', Annals of Economic ans Social Measurment, 5, pp.421–45.

Hausman, J.A. and D.A. Wise (1976) ``Social experimentation, truncated distributions and efficient estimation'', Econometrica, 45, pp.421–5.

Examples


## Simulate a data.frame
n <- 10000
sigma <- 4
alpha <- 2
beta <- 1
x <- rnorm(n,0,2)
eps <- rnorm(n)
y <- alpha+beta*x+eps*sigma
d <- data.frame(y = y, x = x)

## Use a truncated subsample
dl1 <- subset(d, y>1)

## Use truncreg to estimate consistently the model

truncreg(y~x, dl1, point = 1, direction = "left")

[Package truncreg version 0.1-1 Index]