truncreg: Parametric truncated regression for cross-sectional data
In npsf: Nonparametric and Stochastic Efficiency and Productivity Analysis

Description Usage Arguments Details Value Author(s) See Also Examples

truncreg performs maximum likelihood estimation of the parameters in cross-sectional truncated regression.

truncreg(formula, data, subset, 
 ll = -Inf, ul = Inf, 
 lmtol = .Machine$double.eps, maxiter = 150, 
 marg.eff = FALSE, 
 print.level = 1)

`formula`	an object of class “formula” (or one that can be coerced to that class): a symbolic description of the model. The details of model specification are given under ‘Details’.
`data`	an optional data frame containing variables in the model. If not found in data, the variables are taken from environment (`formula`), typically the environment from which `sf` is called.
`subset`	an optional vector specifying a subset of observations for which technical or cost efficiencies are to be computed.
`ll`	scalar or one-sided formula for lower limit for left-truncation; e.g. `ll = ~ 1` or `ll = ~ z1`.
`ul`	scalar or one-sided formula for upper limit for right-truncation; e.g. `ul = ~ 800` or `ul = ~ z1`.
`lmtol`	numeric. Tolerance for the scaled gradient in ML optimization. Default is .Machine$double.eps.
`maxiter`	numeric. maximum number of iteration for maximization of the log likelihood function.
`marg.eff`	logical. If `TRUE`, unit-specific marginal effects of exogenous variables on the mean of distribution of inefficiency term are returned.
`print.level`	numeric. 0 - nothing is printed. 1 - optimization steps and print estimation results. 2 - detailed optimization steps and print estimation results. Default is 1.

truncreg performs a regression from a sample drawn from a restricted part of the population. Under the assumption that the error term of the whole population is normal, the error terms in the truncated regression model have a truncated normal distribution.

Both lower limit for left-truncation and upper limit for right-truncation can be specified simultaneously.

Models for truncreg are specified symbolically. A typical model has the form y ~ x1 + ..., where y represents the left hand side variable and {x1,...} right hand side variables.

If marg.eff = TRUE, the marginal effects are computed.

truncreg returns a list of class npsf containing the following elements:

`call`	call. 'truncreg.cs'.
`model`	character. Unevaluated call to function `truncreg`.
`coef`	numeric. Named vector of ML parameter estimates.
`table`	matrix. Table with results.
`vcov`	matrix. Estimated covariance matrix of ML estimator.
`ll`	numeric. Value of log-likelihood at ML estimates.
`lmtol`	numeric. Convergence criterion: tolerance for the scaled gradient.
`LM`	numeric. Value of the scaled gradient.
`esttime`	numeric. Estimation time.
`marg.effects`	data frame. Contains unit-specific marginal effects of exogenous variables.
`sigma`	numeric. estimate of sigma.
`LL`	numeric. The lower limit for left-truncation
`UL`	numeric. The upper limit for left-truncation
`n`	numeric. Number of observations (used in regression).
`n.full`	numeric. Number of observations (used and not used in regression).
`nontruncsample`	logical. Returns TRUE if the observation in user supplied data is in the estimation subsample and in non-truncated part of the sample, and FALSE otherwise.
`esample`	logical. Returns TRUE if the observation in user supplied data is in the estimation subsample and FALSE otherwise.

Oleg Badunenko <oleg.badunenko@brunel.ac.uk>

teradial, tenonradial, teradialbc, tenonradialbc, nptestrts, nptestind, sf

 
require( npsf )
 
# Female labor force participation dataset
 
data(mroz)
head(mroz)

t1 <- npsf::truncreg(hours ~ kidslt6 + kidsge6 + age*educ, 
 data = mroz, ll = 0, lmtol = 1e-16, print.level = 2)
 
# matrices also can be used
myY <- mroz$hours
myX <- cbind(mroz$kidslt6, mroz$kidsge6, mroz$age, mroz$educ, mroz$age*mroz$educ)

t1.m <- truncreg(myY ~ myX, ll = 0)

# gives identical result to `t1':
# compare summary(t1) and summary(t1.m)

# using variable for limits is admissible
# we obtain the same result as before

mroz$myll <- 0
t11 <- npsf::truncreg(hours ~ kidslt6 + kidsge6 + age*educ, 
 data = mroz, ll = ~ myll, lmtol = 1e-16, print.level = 0)
summary(t11)

# if you believe that the sample is additionally truncted from above at say 3500

t12 <- update(t1, ul = 3500, print.level = 1)

# for obtaining marginal effects

t13 <- update(t12, marg.eff = TRUE)

summary(t13$marg.effects)