Description Usage Arguments Details Value Author(s) References Examples
Fixed and random effects estimators for truncated or censored limited dependent variable
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |
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
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).
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.