pldv  R Documentation 
Fixed and random effects estimators for truncated or censored limited dependent variable
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"),
...
)
formula 
a symbolic description for the model to be estimated, 
data 
a 
subset 
see 
weights 
see 
na.action 
see 
model 
one of 
index 
the indexes, see 
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 ( 
sample 

... 
further arguments. 
pldv
computes two kinds of models: a LSQ/LAD estimator for the
firstdifference model (model = "fd"
) and a maximum likelihood estimator
with an assumed normal distribution for the individual effects
(model = "random"
or "pooling"
).
For maximumlikelihood estimations, pldv
uses internally function
maxLik::maxLik()
(from package maxLik).
For model = "fd"
, an object of class c("plm", "panelmodel")
, for
model = "random"
and model = "pooling"
an object of class c("maxLik", "maxim")
.
Yves Croissant
HONO:92plm
## 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)
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