pldv: Panel estimators for limited dependent variables

In plm: Linear Models for Panel Data

Panel estimators for limited dependent variables

Description

Fixed and random effects estimators for truncated or censored limited dependent variable

Usage

pldv(
  formula,
  data,
  subset,
  weights,
  na.action,
  model = c("fd", "random", "pooling"),
  index = NULL,
  R = 20,
  start = NULL,
  lower = 0,
  upper = +Inf,
  objfun = c("lsq", "lad"),
  sample = c("cens", "trunc"),
  ...
)

Arguments

formula	a symbolic description for the model to be estimated,
data	a data.frame,
subset	see lm,
weights	see lm,
na.action	see lm,
model	one of "fd", "random", or "pooling",
index	the indexes, see pdata.frame(),
R	the number of points for the gaussian quadrature,
start	a vector of starting values,
lower	the lower bound for the censored/truncated dependent variable,
upper	the upper bound for the censored/truncated dependent variable,
objfun	the objective function for the fixed effect model (model = "fd", irrelevant for other values of the model argument ): one of "lsq" for least squares (minimise sum of squares of the residuals) and "lad" for least absolute deviations (minimise sum of absolute values of the residuals),
sample	"cens" for a censored (tobit-like) sample, "trunc" for a truncated sample,
...	further arguments.

Details

pldv computes two kinds of models: a LSQ/LAD estimator for the first-difference model (model = "fd") and a maximum likelihood estimator with an assumed normal distribution for the individual effects (model = "random" or "pooling").

For maximum-likelihood estimations, pldv uses internally function maxLik::maxLik() (from package maxLik).

Value

For model = "fd", an object of class c("plm", "panelmodel"), for model = "random" and model = "pooling" an object of class c("maxLik", "maxim").

Author(s)

Yves Croissant

References

\insertRef

HONO:92plm

Examples

## as these examples take a bit of time, do not run them automatically
## Not run: 
data("Donors", package = "pder")
library("plm")
pDonors <- pdata.frame(Donors, index = "id")

# replicate Landry/Lange/List/Price/Rupp (2010), online appendix, table 5a, models A and B
modA <- pldv(donation ~ treatment +  prcontr, data = pDonors,
            model = "random", method = "bfgs")
summary(modA)
modB <- pldv(donation ~ treatment * prcontr - prcontr, data = pDonors,
            model = "random", method = "bfgs")
summary(modB)

## End(Not run)

plm documentation built on April 9, 2023, 5:06 p.m.