heckit2fit: 2-step Heckman (heckit) estimation

Description

These functions do the actual fitting of tobit-2 (sample selection), tobit-5 (switching regression) and normal-disturbance treatment effect models by the 2-step Heckman (heckit) estimation. They are called by selection or heckit and they are intended for sampleSelection internal use.

Usage

heckit2fit( selection, outcome, data=sys.frame(sys.parent()),
   weights = NULL, inst = NULL,
   printLevel=print.level, print.level = 0,
   maxMethod = "Newton-Raphson" )
heckit5fit( selection, outcome1, outcome2, data = sys.frame(sys.parent()),
   ys = FALSE, yo = FALSE, xs = FALSE, xo = FALSE, mfs = FALSE,
   mfo = FALSE,
   printLevel=print.level, print.level = 0, maxMethod = "Newton-Raphson", ... )
heckitTfit(selection, outcome, data=sys.frame(sys.parent()),
   ys=FALSE, yo=FALSE, xs=FALSE, xo=FALSE, mfs=FALSE, mfo=FALSE,
   printLevel=0, maxMethod="Newton-Raphson", ... )

Arguments

selection

formula for the probit estimation (1st step) (see selection).

outcome

formula to be estimated (2nd step). In case of treatment effect model, it may include the response indicator from selection equation.

outcome1

formula, the first outcome equation.

outcome2

formula, the second outcome equation.

data

a data frame containing the data.

weights

an optional vector of ‘prior weights’ to be used in the fitting process. Should be NULL or a numeric vector. Weights are currently only supported in type-2 models.

inst

an optional one-sided formula specifying instrumental variables for a 2SLS/IV estimation on the 2nd step.

ys, yo, xs, xo, mfs, mfo

logicals. If true, the response (y), model matrix (x) or the model frame (mf) of the selection (s) or outcome (o) equation(s) are returned.

print.level

numeric, values greater than 0 will produce increasingly more debugging information.

maxMethod

character string, a maximisation method supported by maxLik that is used for estimating the probit model (1st stage).

...

currently not used.

Value

see selection.