Two-step and maximum likelihood estimation of Heckman-type sample selection models: standard sample selection models (Tobit-2), endogenous switching regression models (Tobit-5), sample selection models with binary dependent outcome variable, interval regression with sample selection (only ML estimation), and endogenous treatment effects models.
THIS IS THE CHANGELOG OF THE "sampleSelection" PACKAGE
Please note that only the most significant changes are reported here. A full ChangeLog is available in the log messages of the SVN repository on R-Forge.
CHANGES IN VERSION 1.0-4 (2015-11-03)
fixed bug in selection( ..., method = "model.frame" ) for Tobit-5 models (missing "drop = FALSE") that caused model.frame() to return an incorrect variable name if the model is estimated by the ML method and all explanatory variables of the second equation of the outcome model are already included in the first equation of the outcome model; the incorrect variable name caused model.matrix(), residuals(), fitted(), and predict() to fail
The R packages "mvtnorm" and "VGAM" are now 'imported' rather than 'suggested'
minor adjustments due to the upgrade of the maxLik package to version 1.3
several minor improvements that are mostly not visible to users
CHANGES IN VERSION 1.0-2 (2014-06-24)
CHANGES IN VERSION 1.0-0 (2014-06-24)
bug fix: selection() did not pass some arguments to other functions, e.g. argument "inst" was ignored so that the second step of heckit-2 models was estimated by OLS rather than by IV estimation although argument "inst" was specified
fixed bug in residuals.selection( , part = "selection" ) that occured when the dependent variable of the selection model was a factor
fixed bug that made residuals( object, part = "outcome" ) not work if "object" was a sample selection model with a constructed variable (e.g. "log(wage)") estimated by ML
residuals.selection( , part = "outcome" ) now works for models with binary dependent outcome variables
probit models, tobit-2 models, and binary selection models can now be estimated with observation-specific weights
in case of a binary dependent variable of the outcome equation, fitted( object, part = "outcome" ) now returns the probabilities rather than the linear prdictors
added a predict() method for probit models
added a logLik() method for probit models
added nobs() and nObs() methods for probit and selection models
added argument "maxMethod" to heckit2fit() and heckit5fit() that specifies the maximisation method that is used for estimating the probit model (1st stage)
the default df.residual() method works for probit models
this package no longer depends on package 'systemfit' but it imports function systemfit from package "systemfit"
minor adjustments in invMillsRatio() so that it works with bivariate probit models estimated by the current version of the VGAM package (0.9-4)
modified some internal calculations so that they are numerically nore stable
removed the deprecated function tobit2()
CHANGES IN VERSION 0.7-2 (2012-08-14)
fixed a bug in the calculation of the Hessian of the log likelihood function for probit models that only occurred in specific circumstances (reported by Jon K. Peck)
in Tobit 5 models, argument "outcome" can now be a pre-defined list (bug reported by Jon K. Peck)
CHANGES IN VERSION 0.7-0 (2012-03-04)
CHANGES IN VERSION 0.6-12 (2011-11-13)
fixed a bug that occurred in the fitted( ..., part = "outcome" ), model.matrix( ..., part = "outcome" ), and model.frame() methods as well as in selection( ..., method = "model.frame" ) if all regressors of the outcome equation are already included in the selection equation. Thanks to Timothée Carayol for reporting this bug!
fixed partial argument matching in invMillsRatio() and probit()
CHANGES IN VERSION 0.6-10
added a NAMESPACE file
minor modifications so that the sampleSelection package works with the versions >= 0.7-3 of the maxLik package
CHANGES IN VERSION 0.6-8 AND BEFORE