2-step Heckman (heckit) estimation
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", ... )
selection |
formula for the probit estimation (1st step)
(see |
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 ( |
print.level |
numeric, values greater than 0 will produce increasingly more debugging information. |
maxMethod |
character string,
a maximisation method supported by |
... |
currently not used. |
see selection
.
Arne Henningsen, Ott Toomet otoomet@ut.ee
see selection
.
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