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\author{Ott Toomet\\Tartu University
   \And Arne Henningsen\\University of Kiel}
\Plainauthor{Ott Toomet, Arne Henningsen}

\title{Sample Selection Models in \proglang{R}: Package~\pkg{sampleSelection}}
\Plaintitle{Sample Selection Models in R: Package sampleSelection}

\Abstract{ 
  This introduction to the \proglang{R} package \pkg{sampleSelection} 
  is a slightly modified version of \cite{toomet08}, 
  published in the \emph{Journal of Statistical Software}.

   This paper describes the implementation of Heckman-type sample
  selection models in \proglang{R}.  We discuss the sample selection
  problem as well as the Heckman solution to it, and argue that
  although modern econometrics has non- and semiparametric estimation
  methods in its toolbox, Heckman models are an integral part of the
  modern applied analysis and econometrics syllabus.  We describe
  the implementation of these models in the package \pkg{sampleSelection} and
  illustrate the usage of the package on several simulation and real
  data examples.  Our examples demonstrate the effect of exclusion
  restrictions, identification at infinity and misspecification.  We
  argue that the package can be used both in applied research and
  teaching.
}

\Keywords{sample selection models, Heckman selection models, econometrics, \proglang{R}}
\Plainkeywords{sample selection models, Heckman selection models, econometrics, R}

\Address{
  Ott Toomet\\
  Department of Economics\\
  Tartu University\\
  Narva 4-A123\\
  Tartu 51009, Estonia\\
  Telephone: +372/737.6348\\
  E-mail: \email{otoomet@ut.ee}\\
  URL: \url{http://www.obs.ee/~siim/}\\

  Arne Henningsen\\
  Department of Agricultural Economics\\
  University of Kiel\\
  24098 Kiel, Germany\\
  Telephone: +49 431/880/4445\\
  E-mail: \email{ahenningsen@agric-econ.uni-kiel.de}\\
  URL: \url{http://www.uni-kiel.de/agrarpol/ahenningsen/}
}

\begin{document}
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@ 

\section{Introduction}
\label{sec:intro}

Social scientists are often interested in causal effects---what is
the impact of a new drug, a certain type of school or being born as a
twin.  Many of these cases are not under the researcher's control.  
Often, the subjects can decide themselves, whether they take a
drug or which school they attend.  They cannot control whether they
are twins, but neither can the researcher---the twins may tend to be
born in different types of families than singles.  All these cases
are similar from the statistical point of view.  Whatever is the
sampling mechanism, from an initial ``random'' sample we extract a
sample of interest, which may not be representative of the population
as a whole
\citep[see][p.~1937, for a discussion]{heckman+macurdy1986}.

This problem---people who are ``treated'' may be different than the rest
of the population---is usually referred to as a \emph{sample
  selection} or \emph{self-selection} problem.  We cannot estimate the
causal effect, unless we solve the selection
problem\footnote{Correcting for selectivity is necessary but not
  always sufficient for estimating the causal effect.  
  Another common problem is the lack of common support between
  the treated and untreated population.  We are grateful to a referee
  for pointing this out.}.  
Otherwise, we will
never know which part of the observable outcome is related to the
causal relationship and which part is due to the fact that different
people were selected for the treatment and control groups.

Solving sample selection problems requires additional
information.  This information may be in different forms, each of which may
or may not be feasible or useful for any particular case.  
Here we list a few popular choices:

\begin{itemize}
\item Random experiment, the situation where the participants
  do not have control over their status but the researcher
  does.  Randomisation is often the best possible
  method as it is easy to analyse and understand.  However, this
  method is seldom feasible for practical and ethical reasons.  Even
  more, the
  experimental environment may add additional interference which
  complicates the analysis.
\item Instruments (exclusion restrictions) are in many ways similar to
  randomisation.  These are variables, observable to the researcher,
  and which determine the treatment status but not the outcome.
  Unfortunately, these two requirements tend to contradict each other,
  and only rarely do we have instruments of reasonable
  quality.
\item Information about the functional form of the selection and
  outcome processes, such as the distribution of the disturbance
  terms.  The original Heckman's solution belongs to this group.
  However, the functional form assumptions are usually hard to justify.
\end{itemize}

During recent decades, either randomisation or 
pseudo-randomisation (natural experiments) have become state of the art
for estimating causal effects.  However, methods relying on
distributional assumptions are still widely used.  The reason
is obvious---these methods are simple, widely available in software
packages, and they are part of the common econometrics syllabus.
This is true even though reasonable instruments and parametric assumptions can only seldom be
justified, and therefore,
it may be hard to disentangle real causal effects from
(artificial) effects of parametric assumptions.

Heckman-type selection models also serve as excellent teaching tools.
They are extensively explained in many recent econometric
text books \citep[e.g.][]{johnston97, verbeek00, greene2002, wooldridge03,
cameron05} and they are standard procedures in popular software packages
like \pkg{Limdep} \citep{Limdep} and \proglang{Stata} \citep{Stata}.
These
models easily allow us to experiment with selection bias,
misspecification, exclusion restrictions etc.  They are easy
to implement, to visualize, and to understand.

The aim of this paper is to describe the \proglang{R} \citep{r2007}
package \pkg{sampleSelection} (version~0.6-0), which is available on
the Comprehensive \proglang{R} Archive Network 
at \url{http://CRAN.R-project.org/package=sampleSelection}.  The package implements two types
of more popular Heckman selection models which, as far as we know,
were not available for \proglang{R} before.  Our presentation is
geared toward teaching because we believe that one of the advantages of
these types of models lies in econometrics training.

The paper is organized as follows: In the next section we introduce
the \citet{heckman1976} solution to the sample selection problem.
Section~\ref{sec:implementation} briefly describes the current implementation
of the model in \pkg{sampleSelection} and its possible generalisations.
In Section~\ref{sec:using} we
illustrate the usage of the package on various simulated
data sets.  Section~\ref{sec:reliability} is devoted to
replication exercises where we compare
our results to examples in the literature.
Section~\ref{sec:problems} describes robustness issues of the
method and our
implementation of it; and the last section concludes.
% Justin's recommendation: concludes the discussion


\section{Heckman's solution}
\label{sec:heckman}

The most popular solutions for sample selection problems
are based on \citet{heckman1976}.
A variety of generalisations of Heckman's standard sample selection model
can be found in the literature.
These models are also called ``generalized Tobit models''
\citep{amemiya84,amemiya1985}.
A comprehensive classification of these models has been proposed
by \citet{amemiya84,amemiya1985}.


\subsection{Tobit-2 models}
\label{sec:theory-tobit2}

Heckman's standard sample selection model
is also called ``Tobit-2'' model \citep{amemiya84,amemiya1985}.
It consists of the following (unobserved) structural process:
\begin{align}
  y_i^{S*} &= {\vec{\beta}^S}' \vec{x}_i^S + \varepsilon_i^S
  \label{eq:probit*}
  \\
  y_i^{O*} &= {\vec{\beta}^O}' \vec{x}_i^O + \varepsilon_i^O,
  \label{eq:outcome*}
\end{align}
where $y_i^{S*}$ is the realisation of the the latent value of the
selection ``tendency'' for the individual $i$, and $y_i^{O*}$ is the
latent outcome.  $\vec{x}_i^S$ and $\vec{x}_i^O$ are explanatory
variables for the selection and outcome equation, respectively.
$\vec{x}^S$ and $\vec{x}^O$ may or may not be equal.  We observe
\begin{align}
  y_i^S 
  &= 
  \begin{cases}
    0 & \quad \text{if } y_i^{S*} < 0
    \label{eq:probit}
    \\
    1 & \quad \text{otherwise}
  \end{cases}
  \\
  y_i^O
  &= 
  \begin{cases}
    0 & \quad \text{if } y_i^{S} = 0\\
    y_i^{O*} & \quad \text{otherwise},
  \end{cases}
\end{align}
i.e.\ we observe the outcome only if the latent selection variable
$y^{S*}$ is positive.  The observed dependence between $y^O$ and $x^O$
can now be written as
\begin{equation}
  \E [y^O | \vec{x}^O = \vec{x}_i^O , \vec{x}^S = \vec{x}_i^S , y^S = 1] 
  =
  {\vec{\beta}^O}' \vec{x}_i^O 
  +
  \E [ \varepsilon^O|\varepsilon^S \ge -{\vec{\beta}^S}' \vec{x}_i^S ].
\end{equation}
Estimating the model above by OLS gives in general biased results, as
$\E [ \varepsilon^O|\varepsilon^S \ge -{\vec{\beta}^S}' \vec{x}_i^S ]
\not = 0$, unless $\varepsilon^O$ and $\varepsilon^S$ are mean independent
(in this case $\varrho = 0$ in equation~\eqref{eq:correlation} below).

Assuming the error terms follow a bivariate normal distribution:
\begin{equation}
  \begin{pmatrix}
    \varepsilon^S\\
    \varepsilon^O
  \end{pmatrix}
  \sim
  N\left(
    \begin{pmatrix}
      0\\
      0
    \end{pmatrix},
    \begin{pmatrix}
      1 & \varrho\\
      \varrho & \sigma^2
    \end{pmatrix}
  \right),
   \label{eq:correlation}
\end{equation}
we may employ the following simple strategy: find the
expectations $\E [ \varepsilon^O|\varepsilon^S \ge
-{\vec{\beta}^S}' \vec{x}_i^S ]$, also called the \emph{control
  function}, by estimating the selection equations \eqref{eq:probit*}
and \eqref{eq:probit} by probit, and thereafter insert these
expectations into equation \eqref{eq:outcome*} as additional
covariates (see \citealp{greene2002} for details).  Accordingly, we
may write:
\begin{equation}
  y_i^O
  =
  {\vec{\beta}^O}' \vec{x}_i^O 
  + 
  \E [ \varepsilon^O|\varepsilon^S \ge
  -{\vec{\beta}^S}' \vec{x}_i^S ]
  +
  \eta_i
  \equiv
  {\vec{\beta}^O}' \vec{x}_i^O 
  + 
  \varrho \sigma \lambda({\vec{\beta}^S}' \vec{x}_i^S)
  +
  \eta_i
  \label{eq:tobit2_2step}
\end{equation}
where $\lambda(\cdot) = \phi(\cdot)/\Phi(\cdot)$ is commonly referred
to as inverse Mill's ratio, $\phi(\cdot)$ and $\Phi(\cdot)$ are
standard normal density and cumulative distribution functions and
$\eta$ is a new disturbance term, independent of $\vec{x}^{O}$ and $\vec{x}^{S}$.  The
unknown multiplicator $\varrho \sigma$ can be estimated by OLS
($\hat{\beta}^\lambda$).
Essentially, we describe the selection problem as an omitted variable
problem, with $\lambda(\cdot)$ as the omitted variable.  Since the true
$\lambda(\cdot)$s in equation \eqref{eq:tobit2_2step} are generally
unknown, they are replaced by estimated values based on the probit
estimation in the first step.

An estimator of the variance of $\varepsilon^O$ can be obtained by
\begin{equation}
\hat{\sigma}^2
= \frac{\hat{\vec{\eta}}' \hat{\vec{\eta}}}{n^O}
   + \frac{\sum_i \hat{\delta}_i}{n^O} \left. \hat{\beta}^\lambda \right.^2
\end{equation}
where $\hat{\vec{\eta}}$ is the vector of residuals from the OLS
estimation of~\eqref{eq:tobit2_2step}, $n^O$ is the number of
observations in this estimation, and $\hat{\delta}_i = \hat{\lambda}_i
( \hat{\lambda}_i + \left. \hat{\vec{\beta}}^S \right. ' \vec{x}_i^S
)$.  Finally, an estimator of the correlation between $\varepsilon^S$
and $\varepsilon^O$ can be obtained by $\hat{\varrho} =
\hat{\beta}^\lambda / \hat{\sigma}$.  Note that $\hat{\varrho}$ can be
outside of the $[-1, 1]$ interval.

Since the estimation of \eqref{eq:tobit2_2step} is not based on the
true but on estimated values of $\lambda(\cdot)$, the standard OLS
formula for the coefficient variance-covariance matrix is not
appropriate \citep[p.~157]{heckman79}.  A consistent estimate of the
variance-covariance matrix can be obtained by
\begin{equation}
\widehat{\VAR} \left[ \hat{\vec{\beta}}^O, \hat{\vec{\beta}}^\lambda \right]
= \hat{\sigma}^2
   \left[ {\mat{X}_\lambda^O}' \mat{X}_\lambda^O \right]^{-1}
   \left[ {\mat{X}_\lambda^O}'
      \left( \mat{I} - \hat{\varrho}^2 \hat{\mat{\Delta}} \right)
      \mat{X}_\lambda^{O} + \mat{Q} \right]
   \left[ {\mat{X}_\lambda^O}' \mat{X}_\lambda^O \right]^{-1}
\end{equation}
where
\begin{equation}
\mat{Q}
= \hat{\varrho}^2
   \left( {\mat{X}_\lambda^O}' \hat{\mat{\Delta}} \mat{X}^S \right)
   \widehat{\VAR} \left[ \hat{\vec{\beta}}^S \right]
   \left( {\mat{X}^S}' \hat{\mat{\Delta}} \mat{X}_\lambda^O \right),
\end{equation}
$\mat{X}^S$ is the matrix of all observations of $\vec{x}^S$,
$\mat{X}_\lambda^O$ is the matrix of all observations
of $\vec{x}^O$ and $\hat{\lambda}$,
$\mat{I}$ is an identity matrix,
$\hat{\mat{\Delta}}$ is a diagonal matrix with all $\hat{\delta}_i$
on its diagonal, and
$\widehat{\VAR} \left[ \hat{\vec{\beta}}^S \right]$ is the estimated variance
covariance matrix of the probit estimate
\citep{greene81,greene2002}.

This is the original idea by \citet{heckman1976}.  As the model is
fully parametric, it is straightforward to construct a more efficient maximum likelihood (ML)
estimator.  Using the properties of
a bivariate normal distribution, it is easy to show that the log-likelihood can
be written as
\begin{align}
  \loglik 
  & = 
  \sum_{\{i:y_i^S = 0\}} 
  \log \pnorm(-{\vec{\beta}^S}' \vec{x}_i^S ) +
  \\
  & +
  \sum_{\{i:y_i^S = 1\}} \left[
    \log \pnorm \left(\frac{
        {\vec{\beta}^S}' \vec{x}_i^S +
        \displaystyle\frac{\varrho}{\sigma}(y_i^O - {\vec{\beta}^O}' \vec{x}_i^O)}
      {\sqrt{1 - \varrho^2}}
      \right)
      -\frac{1}{2} \log 2\pi - 
      \log \sigma -
      \frac{1}{2} \frac{(y_i^O - {\vec{\beta}^O}' \vec{x}_i^O)^2}{\sigma^2} \right].
\end{align}
The original
article suggests using the two-step solution for exploratory work and
as initial values for ML estimation, since in those
days the cost of the two-step solution was \$15 while that of the
maximum-likelihood solution was \$700
\citep[p.~490]{heckman1976}. %footnote 18, page 490
Nowadays, costs are no longer an issue, however, the two-step solution
allows certain generalisations more easily than ML, and is more robust
in certain circumstances (see Section~\ref{sec:problems} below).

This model and its derivations were introduced in the 1970s and 1980s.
The model is well identified if the exclusion restriction
is fulfilled, i.e.\ if $\vec{x}^S$ includes a component with a
substantial explanatory power but which is not present in $\vec{x}^O$.
This means essentially that we have a valid instrument.  If this is
not the case, the identification is related to the non-linearity of
the inverse Mill's ratio $\lambda(\cdot)$.  The exact form of it stems
from the distributional assumptions.  During the recent decades,
various semiparametric estimation techniques have been increasingly
used in addition to the Heckman model (see \citealp{powell1994},
\citealp{pagan+ullah1999}, and \citealp{li07} for a review).

\subsection{Tobit-5 models}
\label{sec:theory-tobit5}

A straightforward generalisation of the standard sample selection model (Tobit-2)
is the switching regression (Tobit-5) model.
In this case, we have two outcome variables,
where only one of them is observable, depending on the selection process.
Switching regression problems arise
in a wide variety of contexts, e.g.\ in treatment effect, migration or
schooling choice analysis. This type of model consists of a system of
three simultaneous latent equations:
\begin{align}
  y_i^{S*} &= {\vec{\beta}^S}' \vec{x}_i^S + \varepsilon_i^S
  \\
  y_i^{O1*} &= {\vec{\beta}^{O1}}' \vec{x}_i^{O1} + \varepsilon_i^{O1}
  \\
  y_i^{O2*} &= {\vec{\beta}^{O2}}' \vec{x}_i^{O2} + \varepsilon_i^{O2},
\end{align}
where $y^{S*}$ is the selection ``tendency'' as in the case of Tobit-2
models, and $y^{O1*}$ and $y^{O2*}$ are the latent outcomes, only one
of which is observable, depending on the sign of $y^{S*}$.  Hence we
observe
\begin{align}
  y_i^S 
  &= 
  \begin{cases}
    0 & \quad \text{if } y_i^{S*} < 0
    \\
    1 & \quad \text{otherwise}
  \end{cases}
  \label{eq:t5_selection_obs}
  \\
  y_i^O
  &= 
  \begin{cases}
    y_i^{O1*} & \quad \text{if } y_i^{S} = 0\\
    y_i^{O2*} & \quad \text{otherwise}.
  \end{cases}
\end{align}
Assuming that the disturbance terms have a 3-dimensional normal
distribution,
\begin{equation}
  \begin{pmatrix}
    \varepsilon^S\\
    \varepsilon^{O1}\\
    \varepsilon^{O2}
  \end{pmatrix}
  \sim
  N\left(
    \begin{pmatrix}
      0\\
      0\\
      0
    \end{pmatrix},
    \begin{pmatrix}
      1                 & \varrho_1\sigma_1 & \varrho_2\sigma_2 \\
      \varrho_1\sigma_1 & \sigma_1^2        & \varrho_{12}\sigma_1\sigma_2\\
      \varrho_2\sigma_2 & \varrho_{12}\sigma_1\sigma_2 & \sigma_2^2
    \end{pmatrix}
  \right),
\end{equation}
it is straightforward to construct analogous two-step estimators
as in~\eqref{eq:tobit2_2step}.  We may write
\begin{align}
  \E [y^O | \vec{x}^{O1} = \vec{x}_i^{O1} , \vec{x}^S = \vec{x}_i^S , y^S = 0]
  &=
  {\vec{\beta}^{O1}}' \vec{x}_i^{O1} 
  +
  \E [ \varepsilon^{O1}|\varepsilon^S < -{\vec{\beta}^S}' \vec{x}_i^S ]
  \\
  \E [y^O | \vec{x}^{O2} = \vec{x}_i^{O2} , \vec{x}^S = \vec{x}_i^S , y^S = 1]
  &=
  {\vec{\beta}^{O2}}' \vec{x}_i^{O2}
  +
  \E [ \varepsilon^{O2}|\varepsilon^S \ge -{\vec{\beta}^S}' \vec{x}_i^S ]
\end{align}
and hence
\begin{equation}
  y_i^O
  =
  \begin{cases}
    {\vec{\beta}^{O1}}' \vec{x}_i^{O1} 
    -
    \varrho_1 \sigma_1 \lambda(-{\vec{\beta}^S}' \vec{x}_i^S)
    +
    \eta_i^1
    &
    \text{if } y_i^{S} = 0
    \\
    {\vec{\beta}^{O2}}' \vec{x}_i^{O2} 
    + 
    \varrho_2 \sigma_2 \lambda({\vec{\beta}^S}' \vec{x}_i^S)
    +
    \eta_i^2
    &
    \text{otherwise},
  \end{cases}
  \label{eq:tobit5_2step}
\end{equation}
where $\E [ \eta^1 ] = \E [ \eta^2 ] = 0$ and $\lambda(\cdot) =
\phi(\cdot)/\Phi(\cdot)$ is the inverse Mill's ratio which can be
calculated using the probit estimate of~\eqref{eq:t5_selection_obs}.
This system can be estimated as two independent linear models, one for
the case $y^S = 0$ and another for $y^S = 1$.  The log-likelihood for
this problem can be written as
\begin{align}
  \loglik &= -\frac{N}{2}\log 2\pi + \notag\\
  &+ \sum_{\{i:y_i^S = 0\}} \left\{
    -\log\sigma_1
    -\frac{1}{2}\left( \frac{y_i^O - {\vec{\beta}^{O1}}' \vec{x}_i^{O1}}
      {\sigma_1} \right)^2
    +\log \pnorm \left[ 
      -
      \frac
      {{\vec{\beta}^S}' \vec{x}_i^S + \displaystyle \frac{\varrho_1}{\sigma_1}
        \left({y_i^O - {\vec{\beta}^{O1}}' \vec{x}_i^{O1}}
        \right)}
      {\sqrt{ 1 - \varrho_1^2}} \right] \right\} \notag\\
  &+ \sum_{\{i: y_i^S = 1\}} 
  \left\{
    -\log\sigma_2
    -\frac{1}{2}\left( \frac{y_i^O - {\vec{\beta}^{O2}}' \vec{x}_i^{O2}}
      {\sigma_2} \right)^2
    +\log \pnorm \left[ 
      \phantom{-}
      \frac
      {{\vec{\beta}^S}' \vec{x}_i^S + \displaystyle \frac{\varrho_2}{\sigma_2}
        \left({y_i^O - {\vec{\beta}^{O2}}' \vec{x}_i^{O2}}
        \right)}
      {\sqrt{ 1 - \varrho_2^2}} \right] \right\} \notag\\
\end{align}
where $N$ is the total number of observations.  Note
that $\varrho_{12}$ plays no role in this model; the observable
distributions are determined by the correlations $\varrho_1$ and
$\varrho_2$ between the disturbances of the selection equation
($\varepsilon^S$) and the corresponding outcome equation
($\varepsilon^{O1}$ and $\varepsilon^{O2}$).


\section[Implementation]{Implementation in \pkg{sampleSelection}}
\label{sec:implementation}

\subsection{Current implementation}
\label{sec:current_implementation}

The main frontend for the estimation of selection models in
\pkg{sampleSelection} is the command \code{selection}.  It requires a formula for
the selection equation (argument \code{selection}), and a formula (or a list
of two for switching regression models) for the outcome equation
(\code{outcome}).  One can choose the method (\code{method}) to be
either \code{"ml"} for the ML estimation, or \code{"2step"} for
the two-step method.  If the user does not provide initial values
(\code{start}) for the ML estimation,
\code{selection} calculates consistent initial values by the
corresponding two-step method.

While the actual two-step estimation is done by the function
\code{heckit2fit} or \code{heckit5fit} (depending on the model),
the ML estimation is done by \code{tobit2fit} or \code{tobit5fit}.
Note that log-likelihood functions of selection models are in
general not globally concave, and hence one should use a good choice
of initial values (see the example in Section~\ref{sec:convergence}).

The likelihood maximisation is performed by the
\pkg{maxLik} package \citep{r-maxLik},
where the Newton-Raphson
algorithm (implemented as the function \code{maxNR})
using an analytic score vector and an analytic Hessian matrix
is used by default.
This results in a reasonably fast computation even in cases
of tens of thousands observations.  A well-defined model should
converge in less than 15 iterations; in the case of weak
identification this number may be considerably larger.  Convergence
issues may appear at the boundary of the parameter space (if
$|\varrho| \to 1$, see Section~\ref{sec:convergence}).  Other
maximisation algorithms can be chosen by argument
\code{maxMethod}, e.g.\ \code{maxMethod="SANN"} uses a variant
of the robust ``simulated annealing''
stochastic global optimization algorithm proposed by \citet{belisle92}
and \code{maxMethod="BHHH"} uses the Berndt-Hall-Hall-Hausman algorithm
\citep{berndt74}.

The command \code{selection} returns an object of class \code{selection}.
The \pkg{sampleSelection} package provides several methods for objects
of this class:
a \code{print} method prints the estimation results,
\code{summary} method (and associated \code{print} method) calculate and print
summary results,
\code{coef} methods extract the estimated coefficients,
a \code{vcov} method extracts their covariance matrix,
a \code{fitted} method extracts the fitted values,
a \code{residuals} method extracts the residuals,
a \code{model.frame} method extracts the model frame,
and a \code{model.matrix} method extracts the model matrix.

The \code{coef} and \code{vcov} methods for \code{selection} objects,
as well as the \code{print} method for\linebreak
\code{summary.selection} objects
include an extra argument \code{part},
which specifies which part of the model is returned or printed.
One may choose either the full model (\code{part="full"}, default),
or the outcome equation only (\code{part="outcome"}).
The \code{fitted}, \code{residuals}, and \code{model.matrix} methods
also include a \code{part} argument.
However, for these functions the \code{part} argument specifies
whether the objects of the outcome part (\code{part="outcome"}, default)
or of the selection part (\code{part="selection"}) should be returned.

Currently, the variance-covariance matrix of the two-step estimators
is only partially implemented with \code{NA}-s in place of the
unimplemented components.

The package is written completely in \proglang{R} which should
minimize the portability issues.  It depends on packages
\pkg{maxLik} \citep{r-maxLik},
\pkg{systemfit} \citep{henningsen07a,r-systemfit},
and \pkg{mvtnorm} \citep{r-mvtnorm},
where \pkg{mvtnorm} is used for examples only.
All these packages
are available from the Comprehensive \proglang{R} Archive Network at
\url{http://CRAN.R-project.org/}.

\subsection{Current API and a wider range of selection models}

We now briefly discuss possible ways of introducing more general selection
models using a slightly generalized version of the current API.

First, the current argument \code{selection} can be used for
specifying more general selection conditions.  \code{selection} might
contain an interval for interval censoring (for instance
\code{selection = c(0, Inf)} in case of the standard Tobit model),
more than one formula (for multiple treatment models), or an indicator
for the selection mechanism (like \code{"max"} or \code{"min"} for
switching regression with unobserved separator).  In this way, all 
generalized Tobit models listed by \cite{amemiya84,amemiya1985} can be specified.

Another possible generalisation is allowing for discrete dependent
variable models.  While the case of binary-choice can be easily
distinguished from continuous response, we
need an additional parameter in the multiple-choice case.  This parameter
(possibly a vector where components correspond to the individual
equations) will allow the user to specify the exact type of the
response (like multinominal, ordered or Poissonian).

Third, different distributions of the disturbance terms can be
specified in a similar way using an additional parameter.  It may be a
vector if different marginal distributions for different outcome
equations are necessary.

Finally, as the \code{selection} currently supports only two easily
distinguishable models, we have not provided a way to specify the
model explicitly.  However, explicit specification would allow users
to locate the programming problems more easily, and lessen the
complications related to automatic guess of the correct model type.



\section[Usage]{Using the \code{selection} function}
\label{sec:using}

This section provides selected illustrative simulation experiments
which illustrate both the strong and weak sides of the method, and the
typical usage of \code{selection}.


\subsection{Tobit-2 models}
\label{sec:using-tobit2}

First, we estimate a correctly specified Tobit-2 model with exclusion
restriction:
<<code:t2generate>>=
  set.seed(0)
  library("sampleSelection")
  library("mvtnorm")
  eps <- rmvnorm(500, c(0,0), matrix(c(1,-0.7,-0.7,1), 2, 2))
  xs <- runif(500)
  ys <- xs + eps[,1] > 0
  xo <- runif(500)
yoX <- xo + eps[,2]
yo <- yoX*(ys > 0)
@
% -- please keep these comments.  These are necessary for auctex
We use \pkg{mvtnorm} in order to create bivariate normal disturbances
with correlation $-0.7$.  Next, we generate a uniformly distributed
explanatory variable for the selection equation, \code{xs}, the
selection outcome \code{ys} by probit data generating process, and the
explanatory variable for the outcome equation \code{xo}.  All our true
intercepts are equal to 0 and our true slopes are equal to 1, both in this and the
following examples.  We retain the latent outcome variable
(\code{yoX}) for the figure below, and calculate the observable outcome
\code{yo}.  Note that the vectors of explanatory variables for the
selection (\code{xs}) and outcome equation (\code{xo}) are independent
and hence the exclusion restriction is fulfilled.  This can also be
seen from the fact that the estimates are reasonably precise:
<<code:t2summary>>=
  summary( selection(ys~xs, yo ~xo))
@ 
<<echo=FALSE>>=
  m <- selection(ys~xs, yo ~xo)
@
%
One can see that all the true values are within the 95\% confidence
intervals of the corresponding estimates.  

Now look at the graphical representation of the data
(Figure~\ref{fig:t2ex}).  We can see that the unobserved values (empty
circles) tend to have higher $y^{O*}$ realisations than the observed
ones (filled circles).  This is because $\varrho < 0$.  The OLS
estimate (dotted line) is substantially downward biased -- it does not
take into account the fact, that we tend to observe the observations
with low realisations of $\varepsilon^O$.  The slope, however, remains
unbiased because $\E [ \varepsilon^O|\varepsilon^S \ge
-{\vec{\beta}^S}' \vec{x}_i^S ]$ does not depend on $\vec{x}^O$.

\begin{figure}[tp]
\begin{center}
<<fig=TRUE,height=4,echo=FALSE>>=
par(mar=c(3,3,0,0) + 0.1,
    mgp=c(2,1,0))
pch <- c(1, 16)
plot(xo, yoX, pch=pch[1 + ys], cex=0.5, lwd=0.3)
abline(a=0, b=1, lty=1)
# True dependence
abline(a=coef(m)[3], b=coef(m)[4], lty=2)
# Heckman's model
cf <- coef(lm(yo ~ xo, subset=ys==1))
abline(a=cf[1], b=cf[2], lty=3)
# OLS
@ 
%
\caption{\label{fig:t2ex} Sample selection example with exclusion restriction
(filled circles = observable outcomes, empty circles = unobservable outcomes,
solid line = true dependence, dashed line (partly overlapping the solid) = ML estimate above,
dotted line = OLS estimate based on observable outcomes only).}
\end{center}
\end{figure}

Now we repeat the same exercise, but without the exclusion restriction,
generating \code{yo} using \code{xs} instead of \code{xo}.
<<>>=
yoX <- xs + eps[,2]
yo <- yoX*(ys > 0)
summary(selection(ys ~ xs, yo ~ xs))
@
% 
The estimates are still unbiased but standard errors are substantially
larger in this case.  The exclusion restriction---independent information about
the selection process---has a certain identifying power that we now
have lost.  We are solely relying on the functional form
identification.

We illustrate this case with an analogous figure (Figure~\ref{fig:t2}).
The selection model uncovers the true dependence very well.  The OLS
estimate is downward biased because of $\varrho < 0$, as in the
previous case.  However, now the slope is upward biased because $\E [
\varepsilon^O|\varepsilon^S \ge -{\vec{\beta}^S}' \vec{x}_i^S ]$ is
increasing in the single explanatory variable in the outcome model,
$\vec{x}^S$. 

\begin{figure}[tp]
\begin{center}
<<fig=TRUE,height=4,echo=FALSE>>=
par(mar=c(3,3,0,0) + 0.1,
    mgp=c(2,1,0))
pch <- c(1, 16)
plot(xs, yoX, pch=pch[1 + ys], cex=0.5, lwd=0.3)
abline(a=0, b=1, lty=1)
# True dependence
abline(a=coef(m)[3], b=coef(m)[4], lty=2)
# Heckman's model
cf <- coef(lm(yo ~ xs, subset=ys==1))
abline(a=cf[1], b=cf[2], lty=3)
# OLS
@ 
%
\caption{\label{fig:t2} Sample selection example without exclusion restriction
(for symbols see Figure~\ref{fig:t2ex}).}
\end{center}
\end{figure}

In order to identify $\beta^\lambda$ and ${\vec{\beta}^O}'$ without
the exclusion restriction, $\lambda(\cdot)$ must differ from a linear
combination of $\vec{x}^O$ components \citep[see][]{leung+yu1996}.  The
degree of non-linearity in $\lambda(\cdot)$ depends on the variability
in ${\vec{\beta}^S}' \vec{x}^S$ as $\lambda(\cdot)$ is a smooth convex
function (see Figure~\ref{fig:cfunctions}).  Hence the standard errors of the estimates depend on the
variation in the latent selection equation \eqref{eq:probit*}, even
without the exclusion restriction fulfilled.  More variation gives
smaller standard errors\footnote{The exact shape of the function
  $\lambda(\cdot)$ is dependent on the distribution of the
  disturbances.  However, $\E[\varepsilon^{O}|\varepsilon^{S} \ge
    -{\vec{\beta}^{S}}' \vec{x}^{S}] \to 0 \text{ as }
  {\vec{\beta}^{S}}' \vec{x}^{S} \to \infty$ under a wide set of
  distribution functions.  Hence the estimator is less dependent on
  functional form assumptions if the variability in the latent
  selection equation is larger.  This is related to identification at
  infinity \citep{chamberlain1986}.}.  We demonstrate this below:
Change the support of $\vec{x}^S$ from $[0,1]$ to $[-5,5]$:
<<>>=
  xs <- runif(500, -5, 5)
  ys <- xs + eps[,1] > 0
yoX <- xs + eps[,2]
  yo <- yoX*(ys > 0)
  summary( selection(ys ~ xs, yo ~ xs))
@
<<echo=FALSE>>=
  m <- selection(ys ~ xs, yo ~ xs)
@
%
Now all the parameters are precisely estimated, with even higher
precision than in the first example where the exclusion restriction
is fulfilled.  The reason is simple: As one can see from
Figure~\ref{fig:lambda()}, selection is not an issue if $x^S >
2$ while virtually nothing is observed if $x^{S} < -2$. 
Here $\lambda(\beta^S x^S)$
differs enough from a linear function.

\begin{figure}[tp]
\begin{center}
<<fig=TRUE,height=4,echo=FALSE>>=
par(mar=c(3,3,0,0) + 0.1,
    mgp=c(2,1,0))
pch <- c(1, 16)
plot(xs, yoX, pch=pch[1 + ys], cex=0.5, lwd=0.3)
abline(a=0, b=1, lty=1)
# True dependence
abline(a=coef(m)[3], b=coef(m)[4], lty=2)
# Heckman's model
cf <- coef(lm(yo ~ xs, subset=ys==1))
abline(a=cf[1], b=cf[2], lty=3)
# OLS
@ 
%
\caption{\label{fig:lambda()} Sample selection example with more variation in $x^S$ (for
symbols see Figure~\ref{fig:t2ex}).}
\end{center}
\end{figure}



\subsection{Switching regression models}
\label{sec:using-tobit5}

Now let us focus on the Tobit-5 examples.
We create the following simple switching regression problem:
<<>>=
  set.seed(0)
  vc <- diag(3)
  vc[lower.tri(vc)] <- c(0.9, 0.5, 0.1)
  vc[upper.tri(vc)] <- vc[lower.tri(vc)]
  eps <- rmvnorm(500, c(0,0,0), vc)
  xs <- runif(500)
  ys <- xs + eps[,1] > 0
  xo1 <- runif(500)
  yo1 <- xo1 + eps[,2]
  xo2 <- runif(500)
  yo2 <- xo2 + eps[,3]
@ 
%
We generate 3 disturbance vectors by a 3-dimensional normal distribution using \code{rmvnorm}.  We
set the correlation between $\varepsilon^S$ and $\varepsilon^{O1}$
equal to 0.9 and between $\varepsilon^S$ and $\varepsilon^{O2}$ to
0.5.  The third correlation, 0.1, takes care of the positive
definiteness of the covariance matrix and does not affect the results.
Further, we create three independent explanatory variables (\code{xs},
\code{xo1} and \code{xo2}, uniformly distributed on $[0,1]$), and
hence the exclusion restriction is fulfilled.  \code{selection} now
expects three formulas, one for the selection equation, as before, and
a list of two for the outcome equation.  Note that we do not have to
equalize the unobserved values to zero, those are simply ignored.  The
results look as follows:
<<>>=
  summary(selection(ys~xs, list(yo1 ~ xo1, yo2 ~ xo2)))
@ 
We can see that the parameters are fairly well estimated.  All the
estimates are close to the true values.

Next, take an example of functional form misspecification.  We create
the disturbances as 3-variate $\chi_1^2$ random variables (we subtract
1 in order to get the mean zero disturbances), and generate \code{xs}
to be in the interval $[-1,0]$ in order to get an asymmetric distribution
over observed choices:
<<>>=  
set.seed(5)
eps <- rmvnorm(1000, rep(0, 3), vc)
eps <- eps^2 - 1
xs <- runif(1000, -1, 0)
ys <- xs + eps[,1] > 0
xo1 <- runif(1000)
yo1 <- xo1 + eps[,2]
xo2 <- runif(1000)
yo2 <- xo2 + eps[,3]

summary(selection(ys~xs, list(yo1 ~ xo1, yo2 ~ xo2), iterlim=20))
@
%
Although we still have an exclusion restriction, now serious problems
appear---most intercepts are statistically significantly different from the
true values zero.  This model has serious convergence problems and
often it does not converge at all (this is why we increased the
\code{iterlim} and used \code{set.seed(5)}).

As the last Tobit example, we repeat the previous exercise without
the exclusion restriction, and a slightly larger variance of \code{xs}:
<<code:t5chi_woER>>=
set.seed(6)
xs <- runif(1000, -1, 1)
  ys <- xs + eps[,1] > 0
  yo1 <- xs + eps[,2]
  yo2 <- xs + eps[,3]
summary(tmp <- selection(ys~xs, list(yo1 ~ xs, yo2 ~ xs), iterlim=20))
@
%
In most cases, this model does not converge.  However, if it does
(like in this case, where we use \code{set.seed(6)}), the results may
be seriously biased.  Note that the first outcome parameters have low
standard errors, but a substantial bias.  We present the graph of the
correct control function, based on the $\chi^2$ distribution, and one
where we assume the normal distribution of the disturbance terms in
Figure~\ref{fig:cfunctions}.  We use the estimated parameters for
constructing the latter, however, we scale the normal control
functions (inverse Mill's ratios) to a roughly similar scale as the
correct ones.
\begin{figure}[tp]
\begin{center}
<<fig=TRUE,height=4,echo=FALSE>>=
   EUlower <- function(alpha) {
      alpha[alpha >= 1] <- NA
      alpha <- sqrt(-alpha + 1)
      EUalpha <- ss^2 - 2*ss*alpha*dnorm(alpha/ss)/(1 - 2*pnorm(-alpha/ss))
      s1s^2/ss^2*EUalpha + s1^2 - s1s^2/ss^2 - 1
   }
   EUupper <- function(alpha) {
      alpha[alpha >= 1] <- 1
      alpha <- sqrt(-alpha + 1)
      EUalpha <- (ss*alpha*dnorm(alpha/ss) + ss^2*(1 - pnorm(alpha/ss)))/(1 - pnorm(alpha/ss))
      s2s^2/ss^2*EUalpha + s2^2 - s2s^2/ss^2 - 1
   }
   Nlower <- function(alpha) {
      alpha <- -alpha
      -Ns1s*dnorm(alpha)/pnorm(alpha)
   }
   Nupper <- function(alpha) {
      alpha <- -alpha
      Ns2s*dnorm(-alpha)/pnorm(-alpha)
   }
   ss <- sqrt(vc[1,1])
   s1 <- sqrt(vc[2,2])
   s2 <- sqrt(vc[3,3])
   s1s <- vc[1,2]
   s2s <- vc[1,3]
   Ns1s <- coef(tmp)["sigma1"]*coef(tmp)["rho1"]
   Ns2s <- coef(tmp)["sigma2"]*coef(tmp)["rho2"]
hatb1O <- coef(tmp)[c("XO1(Intercept)", "XO1xs")]
hatb2O <- coef(tmp)[c("XO2(Intercept)", "XO2xs")]
   es <- eps[,1]
   e1 <- eps[,2]
e2 <- eps[,3]
   ex <- seq(-5, 5, length=200)
ey <- cbind(EUlower(ex), EUupper(ex),
##            -31*Nupper(cbind(1, ex)%*%hatb1O), 5.5*Nlower(cbind(1,ex)%*%hatb2O),
            -s1s*dnorm(-ex)/pnorm(-ex), 
            s2s*dnorm(ex)/pnorm(ex)
            )
   mcy <- matrix(0, length(ex), 2)
   for(i in seq(length=nrow(mcy))) {
      mcy[i,1] <- mean(e1[es < -ex[i]])
      mcy[i,2] <- mean(e2[es > -ex[i]])
   }
par(cex=0.8, mar=c(3,3,0,0) + 0.1, mgp=c(2,1,0))
matplot(ex, ey, type="l", lty=c(1,1,2,2,3,3), col=1,
        xlab=expression(x^S), ylab="",
        ylim=c(-1.5,2.5))
abline(v=-1,lty=3)
abline(v=1, lty=3)
#   matpoints(ex, mcy, pch=c(1,2), cex=0.5, col=1)
   axis(4)
text(-3.5, 0.8,
     expression(paste("correct  ")*E*group("[", epsilon^{O2}*group("|", epsilon^S > -bold(beta)^S*minute*bold(x)^S, ""), "]")))
text(-3.6, -0.4,
     expression(paste("correct  ")*E*group("[", epsilon^{O1}*group("|", epsilon^S < -bold(beta)^S*minute*bold(x)^S, ""), "]")))
text(-2.5, 1.5,
     expression(paste("assumed  ")*E*group("[", epsilon^{O2}*group("|",
         epsilon^S > -bold(beta)^S*minute*bold(x)^S, ""), "]")), pos = 4 )
text(1, -1.4,
     expression(paste("assumed  ")*E*group("[", epsilon^{O1}*group("|",
         epsilon^S < -bold(beta)^S*minute*bold(x)^S, ""), "]")), pos = 2 )
@ 
%
\caption{\label{fig:cfunctions} Correct and assumed control functions. Dotted vertical
lines denote the support of $x^S$ in the simulation experiment;
correct control functions are based on the $\chi^2(1)$ distribution;
assumed control functions are based on the normal distribution.
}
\end{center}
\end{figure}

One can see that the functions differ substantially in the relevant
range of $x^S \in [-1, 1]$.  In particular, the true
$\E [ \varepsilon^{O2}|\varepsilon^S \ge -{\vec{\beta}^S}' \vec{x}^S ]$ decreases
substantially faster close to $x^S = 1$ than the normal approximation
while the correct
$\E [ \varepsilon^{O1}|\varepsilon^S < -{\vec{\beta}^S}' \vec{x}^S ]$
is decreasing slower compared to the
approximation.

It is instructive to estimate the same model as two independent OLS
equations:
<<>>=
  coef(summary(lm(yo1~xs, subset=ys==0)))
  coef(summary(lm(yo2~xs, subset=ys==1)))
@ 
%
One can see that the OLS estimates are very close to the ML ones.
This is related to the fact that none of the $\varrho$s is
statistically significantly different from zero.


\section{Two replication exercises}
\label{sec:reliability}

In this section we test the reliability of the results from
\code{selection} by applying the two-step and the ML estimation method
re-estimating selected models already published in the literature.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Greene (2002): Example 22.8, page 786}
\label{sec:Greene22.8}

The first test is example 22.8 from \citet[p.~786]{greene2002}.  The
data set used in this example is included in \pkg{sampleSelection}; it is
called \code{Mroz87}.  This data set was used by \citet{mroz1987} for
analysing female labour supply.  In this example, labour force
participation (described by dummy \code{lfp}) is modelled by a
quadratic polynomial in age (\code{age}), family income
(\code{faminc}, in 1975 dollars), presence of children (\code{kids}),
and education in years (\code{educ}).  The wage equation includes a
quadratic polynomial in experience (\code{exper}), education in years
(\code{educ}), and residence in a big city (\code{city}).  First, we
have to create a dummy variable for presence of children.
<<greene22.8start>>=
data( "Mroz87" )
Mroz87$kids <- ( Mroz87$kids5 + Mroz87$kids618 > 0 )
@
%
Now, we estimate the model by the two-step method.
<<greene22.8TwoStep>>=
greeneTS <- selection( lfp ~ age + I( age^2 ) + faminc + kids + educ,
   wage ~ exper + I( exper^2 ) + educ + city,
   data = Mroz87, method = "2step" )
@
%
Most results are identical to the values reported by
\citet[p.~786]{greene2002}.
Only the coefficient of the inverse Mill's ratio ($\beta^\lambda = \varrho \sigma$),
its standard error, and $\varrho$ deviate from the published results,
but all differences are less than one percent.%
\footnote{
Note that the standard error of the coefficient of the inverse Mill's ratio
($\beta^\lambda = \varrho \sigma$) is wrong in \citet[p.~786]{greene2002}
\citep[see][]{greene06}.
}

Finally, we repeat the analysis with the ML estimation method:
<<greene22.8ML>>=
greeneML <- selection( lfp ~ age + I( age^2 ) + faminc + kids + educ,
   wage ~ exper + I( exper^2 ) + educ + city, data = Mroz87,
   maxMethod = "BHHH", iterlim = 500 )
@
%
Again, the estimated coefficients and standard errors are almost identical
to the values published in \citet{greene06}.
While we can obtain the same coefficients with the Newton-Raphson (NR)
maximisation method,
we have to use the Berndt-Hall-Hall-Hausman (BHHH) method
to obtain the published standard errors\footnote{We are grateful to
  William Greene for pointing this out.}.
This is because different ways of calculating the Hessian
matrix may result in substantially different standard errors
\citep{calzolari+fiorentini1993}. The NR algorithm uses exact
analytic Hessian, BHHH uses
outer product approximation.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Cameron and Trivedi (2005): Section 16.6, page 553}
\label{sec:Cameron16.6}

The data set used in this example is based on the ``RAND Health
Insurance Experiment'' \citep{newhouse99}.  It is included in
\pkg{sampleSelection}, where it is called \code{RandHIE}.
\citet[p.~553]{cameron05} use these data to analyse health
expenditures.  The endogenous variable of the outcome equation
measures the log of the medical expenses of the individual
(\code{lnmeddol}) and the endogenous variable of the selection
equation indicates whether the medical expenses are positive
(\code{binexp}).  The regressors are the log of the coinsurance rate
plus 1 (\code{logc = log(coins+1)}), a dummy for individual deductible
plans (\code{idp}), the log of the participation incentive payment
(\code{lpi}), an artificial variable (\code{fmde} that is 0 if
\code{idp}\,=\,1 and \code{ln(max(1,mde/(0.01*coins)))} otherwise
(where \code{mde} is the maximum expenditure offer), physical
limitations (\code{physlm}), the number of chronic diseases
(\code{disea}), dummy variables for good (\code{hlthg}), fair
(\code{hlthf}), and poor (\code{hlthp}) self-rated health (where the
baseline is excellent self-rated health), the log of family income
(\code{linc}), the log of family size (\code{lfam}), education of
household head in years (\code{educdec}), age of the individual in
years (\code{xage}), a dummy variable for female individuals
(\code{female}), a dummy variable for individuals younger than 18
years (\code{child}), a dummy variable for female individuals younger
than 18 years (\code{fchild}), and a dummy variable for black
household heads (\code{black}).  First, we select the subsample (study
year \code{year} equal to 2 and education information present) that
is used by \citet{cameron05} and specify the selection as well as the
outcome equation.
%
<<cameron16.6start>>=
data( "RandHIE" )
subsample <- RandHIE$year == 2 & !is.na( RandHIE$educdec )
selectEq <- binexp ~ logc + idp + lpi + fmde + physlm + disea +
   hlthg + hlthf + hlthp + linc + lfam + educdec + xage + female +
   child + fchild + black
outcomeEq <- lnmeddol ~ logc + idp + lpi + fmde + physlm + disea +
   hlthg + hlthf + hlthp + linc + lfam + educdec + xage + female +
   child + fchild + black
@
%
Now, we estimate the model by the two-step method (reporting only the coefficients):
<<cameron16.6TwoStep>>=
rhieTS <- selection( selectEq, outcomeEq, data = RandHIE[ subsample, ],
                          method = "2step" )
@
%
All coefficients and standard errors are fully identical
to the results reported by \citet{cameron05}
--- even if they are compared with the seven-digit values
in their \proglang{Stata} output that is available on
\url{http://cameron.econ.ucdavis.edu/mmabook/mma16p3selection.txt}.%
\footnote{
The coefficient and t-value of \code{idp} in column \code{lnmed}
of \citeauthor{cameron05}'s Table~16.1 seem to be wrong,
because they differ from their \proglang{Stata} output
as well as from our results.
}

Again, we repeat the analysis with the ML estimation method:
<<cameron16.6ML>>=
rhieML <- selection( selectEq, outcomeEq, data = RandHIE[ subsample, ] )
@
%
All coefficients and standard errors of the ML estimation are nearly
identical to the values reported in Table~16.1 of \citet{cameron05} as
well as to the seven-digit values in their \proglang{Stata} output.
Only a few coefficients deviate at the seventh decimal place.
% outcome equation: hlthp, female, child, black


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Robustness issues}
\label{sec:problems}

\subsection{Convergence}
\label{sec:convergence}

The log-likelihood function of the models above is not globally concave.  The
model may not converge, or it may converge to a local maximum, if the
initial values are not chosen well enough.  This may easily happen
as we illustrate below.  Recall example 22.8 from
\citet[p.~786]{greene2002} (section:~\ref{sec:Greene22.8}).  This
model gives reasonable results, but these are sensitive to the
start values.  Now we re-estimate the model specifying start values
``by hand'' (note that you have to supply a positive initial value for
the variance):
<<Greene22.8NoConvergence>>=
greeneStart <- selection( lfp ~ age + I( age^2 ) + faminc + kids + educ,
   wage ~ exper + I( exper^2 ) + educ + city,
   data = Mroz87, start = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.9))

cat( greeneStart$message )

coef( summary( greeneStart ) )[ "rho", ]
@ 
%
The process did not converge.  In the current case the problem lies
with the numerical problems at the boundary of the parameter space
(note that $\varrho$ is close to 1).  A workaround is to use a more
robust maximisation method.  For instance, one may choose to run the SANN
maximizer for 10\,000 iterations, and then use the returned coefficients
as start values for the Newton-Raphson algorithm.%
\footnote{One has to choose a suitable value for \code{parscale};
\code{parscale=0.001} worked well for this example.}
<<Greene22.8SANN>>=
set.seed(0)
greeneSANN <- selection( lfp ~ age + I( age^2 ) + faminc + kids + educ,
   wage ~ exper + I( exper^2 ) + educ + city,
   data = Mroz87, start = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.9),
   maxMethod="SANN", parscale = 0.001 )

greeneStartSANN <- selection( lfp ~ age + I( age^2 ) + faminc + kids + educ,
   wage ~ exper + I( exper^2 ) + educ + city,
   data = Mroz87, start = coef( greeneSANN ) )

cat( greeneStartSANN$message )
@
%
The new Newton-Raphson estimate converged to another maximum
with a log-likehood value even higher than the one of the original estimate
published in \citet[p.~786]{greene2002} (see Section~\ref{sec:Greene22.8}):
<<>>=
logLik( greeneML )
logLik( greeneStartSANN )
@
%
However, in most cases the 2-step method does a good job
in calculating initial values.

\subsection{Boundary of the parameter space}
\label{sec:boundary}

In general, one should prefer \code{method="ml"} instead of
\code{"2step"}.  However, ML estimators may have problems at the boundary of the
parameter space.  Take the textbook Tobit example:
\begin{equation}
  \label{eq:tobit}
  y_i^* = \vec{\beta}' \vec{x}_i + \varepsilon_i;
  \quad
  y_i =
  \begin{cases}
    y_i^* \quad & \text{if } y_i^* > 0\\
    0           & \text{otherwise.}
  \end{cases}
\end{equation}
This model can be written as a Tobit-2 model where the error term of
the selection and outcome equation are perfectly correlated.  In this
case the ML estimator may not converge:
<<tobit_tobit2>>=
set.seed(0)
x <- runif(1000)
y <- x + rnorm(1000)
ys <- y > 0
tobitML <- selection(ys~x, y~x)
cat( tobitML$message )
coef( summary( tobitML ) )[ "rho", ]
@ 
%
The reason, as in the previous example,
is that $\varrho = 1$ lies at the boundary of the parameter
space.  However, the 2-step method still works, although standard
errors are large and $\rho \notin [-1,1]$:
<<tobit_tobit2_summary>>=
tobitTS <- selection(ys~x, y~x, method="2step")
coef( summary( tobitTS ) )[ "rho", ]
@ 
%


\section{Conclusions}
\label{sec:conclusions}

This paper describes Heckman-type selection models and their
implementation in the package \pkg{sampleSelection} for the programming
language \proglang{R}.  These models are popular in estimating impacts
of various factors in economics and other social sciences.  We argue
that they also serve as useful teaching tools because they are easy
to implement and understand.

We describe the implementation and usage of standard sample selection (Tobit-2) and
switching regression (Tobit-5) models in \pkg{sampleSelection}, and possible
generalisations of our \code{selection} function.  We
demonstrate the usage of the package using a number of simulated and
real data examples.  The examples illustrate several important issues
related to exclusion restrictions, identification at infinity, and
functional form specification.  Our implementation works well for
correctly specified cases with the exclusion restriction fulfilled.  The
problems appearing in the case of misspecification or weak
identification are related to the model itself.  In these problematic
cases, the user may gain from a more robust maximisation algorithm.
In some cases,
the two-step estimator is preferable.


\section*{Acknowledgments}

The authors thank Roger Koenker, Achim Zeileis, and two anonymous referees
for helpful comments and suggestions.
Ott Toomet is grateful to the project TMJRI 0525 2003-2007 of the
Estonian Ministry of Education and Science.

\bibliography{selection}

\end{document}
