tobit2 {micEcon} | R Documentation |
Heckman-type selection models are regressions with at least two latent equation and multivariate normal error term. Tobit-2 model is one particular type of those models (Amemiya 1985).
tobit2(selection, formula, data = sys.frame(sys.parent()), method="ml", b0=NULL, print.level=0, y1=FALSE, z=FALSE, y2=FALSE, x=FALSE, model=FALSE, ...)
selection |
a symbolic formula for the selection equation |
formula |
a symbolic formula for the equation of interest |
data |
an optional data frame containing the variables in the model. By default the variables are taken from environment(formula), typically the environment from which the function is called |
method |
the method of calculation: Maximum Likelihood (ml ) or
Heckman 2-step (2step ) |
b0 |
numeric, initial values of the parameters. The order is as follows: gamma (selection equation), beta (the equation of interest), sigma, rho (distribution of the error terms) |
print.level |
information about calculations. 0 – nothing is
printed, bigger numbers give more information.
print.level is sent further to the maximisation algorithm,
see maxNR . |
y1, z, y2, x, model |
logicals. If TRUE the corresponding components of the fit (the selection response, the selection model matrix, the equation response, the equation model matrix and both model frames) are returned. |
... |
further arguments to the maximisation algorithm,
see maxNR . |
Tobit 2 models have two latent variable regressions with jointly normal errors. If selection variable y1 = 0, the formula response y2 is not observable; if y1 = 1, then y2 = y2*. Values for unobservable selection are undefined, they are commonly assumed to be equal to 0. In the current function the values must be there but they are not used.
a list of class tobit2
:
maximum |
likelihood value at maximum |
estimate |
(named) vector of estimates |
twoStep |
object of class "heckit", results of two-step estimation |
NParam |
total number of parameters |
NObs |
total number of observations |
N1 |
number of observations where the response was not observed |
N2 |
number of observations where the response was observed |
NZ |
number of variables in the selection equation |
NX |
number of variables in the main formula |
df |
resulting degrees of freedom |
call, terms1, terms2 |
call and terms of the model |
Plus the components of maxLik
.
These models rely heavily on the parametric assumptions. Do not use them unless the exclusion restriction holds!
Ott Toomet siim@obs.ee
Amemiya 1985
heckit
for the two-step estimation and
probit
for probit models
## uniform observable covariates (note that exclusion restriction is ## fulfilled)! myData <- data.frame( z = runif(500), x = runif(500) ) ## latent selection variable myData$y1s <- 2 * myData$z + rnorm(500) myData$y1 <- myData$y1s > 0 ## latent regressions myData$y2s <- myData$x + 0.5 * rnorm(500) myData$y2 <- ifelse( myData$y1 == 1, myData$y2s, 0) plot( myData$z, myData$y2 ) a <- tobit2( y1~z, y2~x, data = myData ) summary(a)