ivpml {Rchoice} | R Documentation |
Estimate Instrumental Variable Probit model by Maximum Likelihood.
Description
Estimation of Probit model with one endogenous and continuous variable by Maximum Likelihood.
Usage
ivpml(formula, data, messages = TRUE, ...)
## S3 method for class 'ivpml'
terms(x, ...)
## S3 method for class 'ivpml'
model.matrix(object, ...)
## S3 method for class 'ivpml'
estfun(x, ...)
## S3 method for class 'ivpml'
bread(x, ...)
## S3 method for class 'ivpml'
vcov(object, ...)
## S3 method for class 'ivpml'
df.residual(object, ...)
## S3 method for class 'ivpml'
coef(object, ...)
## S3 method for class 'ivpml'
logLik(object, ...)
## S3 method for class 'ivpml'
print(x, ...)
## S3 method for class 'ivpml'
summary(object, eigentol = 1e-12, ...)
## S3 method for class 'summary.ivpml'
print(x, digits = max(3, getOption("digits") - 2), ...)
## S3 method for class 'ivpml'
predict(object, newdata = NULL, type = c("xb", "pr", "stdp"), asf = TRUE, ...)
Arguments
formula |
a symbolic description of the model of the form |
data |
the data of class |
messages |
if |
... |
arguments passed to |
x , object |
an object of class |
eigentol |
the standard errors are only calculated if the ratio of the smallest and largest eigenvalue of the Hessian matrix is less than |
digits |
the number of digits. |
newdata |
optionally, a data frame in which to look for variables with which to predict. |
type |
the type of prediction required. The default, |
asf |
if |
Details
The IV probit for cross-sectional data has the following structure:
y_{1i}^* = x_i^\top\beta + \gamma y_{2i}+ \epsilon_i,
with
y_{2i} = z_i^\top\delta + \upsilon_i,
where y_{1i}^*
is the latent (unobserved) dependent variable for individual i = 1,...,N
;
y_{2i}
is the endogenous continuous variable; z_i
is the vector of exogenous variables
which also includes the instruments for y_{2i}
;
and (\epsilon, \upsilon)
are normal jointly distributed.
The model is estimated using the maxLik
function from maxLik
package using
analytic gradient.
Author(s)
Mauricio Sarrias.
References
Greene, W. H. (2012). Econometric Analysis. 7 edition. Prentice Hall.
Examples
# Data
library("AER")
data("PSID1976")
PSID1976$lfp <- as.numeric(PSID1976$participation == "yes")
PSID1976$kids <- with(PSID1976, factor((youngkids + oldkids) > 0,
levels = c(FALSE, TRUE),
labels = c("no", "yes")))
# IV probit model by MLE
# (nwincome is endogenous and heducation is the additional instrument)
PSID1976$nwincome <- with(PSID1976, (fincome - hours * wage)/1000)
fiml.probit <- ivpml(lfp ~ education + experience + I(experience^2) + age +
youngkids + oldkids + nwincome |
education + experience + I(experience^2) + age +
youngkids + oldkids + heducation,
data = PSID1976)
summary(fiml.probit)