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%\VignetteKeywords{nonparametric, kernel, econometrics, qualitative,
%categorical}
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\author{Tristen Hayfield\\ETH Z\"urich
  \And Jeffrey S.~Racine\\McMaster University}

\title{The \pkg{np} Package}

\Plainauthor{Tristen Hayfield, Jeffrey S.~Racine}

\Plaintitle{The np Package}

%\Shorttitle{The np Package}

\Abstract{ 
  
  We describe the \proglang{R} \pkg{np} package via a series of
  applications that may be of interest to applied econometricians.
  This vignette is based on \citet{v27i01:Hayfield+Racine:2008}.  The
  \pkg{np} package implements a variety of nonparametric and
  semiparametric kernel-based estimators that are popular among
  econometricians. There are also procedures for nonparametric tests
  of significance and consistent model specification tests for
  parametric mean regression models and parametric quantile regression
  models, among others. The \pkg{np} package focuses on kernel methods
  appropriate for the mix of continuous, discrete, and categorical
  data often found in applied settings. Data-driven methods of
  bandwidth selection are emphasized throughout, though we caution the
  user that data-driven bandwidth selection methods can be
  computationally demanding.

}

\Keywords{nonparametric, semiparametric, kernel smoothing,
  categorical data.}

\Plainkeywords{Nonparametric, kernel, econometrics, qualitative,
  categorical}

%% \Volume{13}
%% \Issue{9}
%% \Month{September}
%% \Year{2004}
%% \Submitdate{2004-09-29}
%% \Acceptdate{2004-09-29}

\Address{Tristen Hayfield\\
  ETH H\"onggerberg Campus\\
  Physics Department\\
  CH-8093 Z\"urich, Switzerland\\
  E-mail: \email{hayfield@phys.ethz.ch}\\
  URL: \url{http://www.exp-astro.phys.ethz.ch/hayfield/}\\
  ~\\
  Jeffrey S.~Racine\\
  Department of Economics\\
  McMaster University\\
  Hamilton, Ontario, Canada, L8S 4L8\\
  E-mail: \email{racinej@mcmaster.ca}\\
  URL: \url{http://www.mcmaster.ca/economics/racine/}\\
}

\begin{document}

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\section{Introduction}

Devotees of \proglang{R} \citep{R} are likely to be aware of a number
of nonparametric kernel\footnote{A `kernel' is simply a weighting
  function.} smoothing methods that exist in \proglang{R} base (e.g.,
\code{density}) and in certain \proglang{R} packages (e.g.,
\code{locpoly} in the \pkg{KernSmooth} package
\citep{R-KernSmooth-package}). These routines deliver nonparametric
smoothing methods to a wide audience, allowing \proglang{R} users to
nonparametrically model a density or to conduct nonparametric local
polynomial regression, by way of example.

The appeal of nonparametric methods, for applied researchers at least,
lies in their ability to reveal structure in data that might be missed
by classical parametric methods. Nonparametric kernel smoothing
methods are often, however, much more computationally demanding than
their parametric counterparts.

In applied settings we often encounter a combination of categorical
and continuous datatypes.  Those familiar with traditional
nonparametric kernel smoothing methods will appreciate that these
methods presume that the underlying data is continuous in nature,
which is frequently not the case.  One approach towards handling the
presence of both continuous and categorical data is called a
`frequency' approach, whereby data is broken up into subsets (`cells')
corresponding to the values assumed by the categorical variables, and
only then do you apply say \code{density} or \code{locpoly} to the
continuous data remaining in each cell.  Nonparametric frequency
approaches are widely acknowledged to be unsatisfactory as they often
lead to substantial efficiency losses arising from the use of sample
splitting, particularly when the number of cells is large.

Recent theoretical developments offer practitioners a variety of
kernel-based methods for categorical data only (i.e., unordered and
ordered factors), or for a mix of continuous and categorical
data. These methods have the potential to recapture the efficiency
losses associated with nonparametric frequency approaches as they do
not rely on sample splitting, rather, they smooth the categorical
variables in an appropriate manner; see \citet{LI_RACINE:2007} and the
references therein for an in-depth treatment of these methods, and see
also the references listed in the bibliography.

The \pkg{np} package implements recently developed kernel methods that
seamlessly handle the mix of continuous, unordered, and ordered factor
datatypes often found in applied settings. The package also allows the
user to create their own routines using high-level function calls
rather than writing their own \proglang{C} or \proglang{Fortran}
code.\footnote{The high-level functions found in the package in turn
  call compiled \proglang{C} code allowing the user to focus on the
  application rather than the implementation details.}  The design
philosophy underlying \pkg{np} aims to provide an intuitive, flexible,
and extensible environment for applied kernel estimation. We
appreciate that there exists tension among these goals, and have tried
to balance these competing ends, with varying degrees of success.
\pkg{np} is available from the Comprehensive \proglang{R} Archive
Network at \url{http://CRAN.R-project.org/package=np}.

Currently, a range of methods can be found in the \pkg{np} package
including unconditional (\citeauthor{LI_RACINE:2003a}
\citeyear{LI_RACINE:2003a}, \citeauthor{OUYANG_LI_RACINE:2006}
\citeyear{OUYANG_LI_RACINE:2006}) and conditional
(\citeauthor{HALL_RACINE_LI:2004} \citeyear{HALL_RACINE_LI:2004},
\citeauthor{RACINE_LI_ZHU:2004} \citeyear{RACINE_LI_ZHU:2004}) density
estimation and bandwidth selection, conditional mean and gradient
estimation (local constant \citeauthor{RACINE_LI:2004}
\citeyear{RACINE_LI:2004}, \citeauthor{HALL_LI_RACINE:2007}
\citeyear{HALL_LI_RACINE:2007} and local polynomial
\citeauthor{LI_RACINE:2004} \citeyear{LI_RACINE:2004}), conditional
quantile and gradient estimation (\citeauthor{LI_RACINE:2008}
\citeyear{LI_RACINE:2008}), model specification tests (regression
\citeauthor{HSIAO_LI_RACINE:2007} \citeyear{HSIAO_LI_RACINE:2007},
quantile), significance tests (\citeauthor{RACINE:1997}
\citeyear{RACINE:1997}, \citeauthor{RACINE_HART_LI:2006}
\citeyear{RACINE_HART_LI:2006}), semiparametric regression (partially
linear \citeauthor{ROBINSON:1988} \citeyear{ROBINSON:1988},
\citeauthor{GAO_LIU_RACINE:2012} \citeyear{GAO_LIU_RACINE:2012}, index models
\citeauthor{KLEIN_SPADY:1993} \citeyear{KLEIN_SPADY:1993},
\citeauthor{ICHIMURA:1993} \citeyear{ICHIMURA:1993}, average
derivative estimation, varying/smooth coefficient models
\citeauthor{LI_RACINE:2010} \citeyear{LI_RACINE:2010},
\citeauthor{LI_OUYANG_RACINE:2012} \citeyear{LI_OUYANG_RACINE:2012}),
among others. The various functions in the \pkg{np} package are
described in Table~\ref{np function table}.


\begin{table}[!ht]
  \centering\raggedright
  {\small
    \begin{tabular}{p{.75in}|p{3in}|p{2in}}
			
			\hline
			\textbf{Function} & \textbf{Description} & \textbf{Reference} \\ 
			\hline
			
\code{npcdens} & Nonparametric Conditional Density Estimation & \citet{HALL_RACINE_LI:2004} \\
\code{npcdensbw} &  Nonparametric Conditional Density Bandwidth Selection & \citet{HALL_RACINE_LI:2004}\\
\code{npcdist} &  Nonparametric Conditional Distribution Estimation & \citet{LI_RACINE:2008}\\
\code{npcmstest} &   Parametric Model Specification Test & \citet{HSIAO_LI_RACINE:2007}\\
\code{npconmode} &  Nonparametric Modal Regression & \\
\code{npdeneqtest} &  Nonparametric Test for Equality of Densities & \citet{LI_MAASOUMI_RACINE:2009} \\
\code{npdeptest} &  Nonparametric Entropy Test for Pairwise Dependence & \citet{MAASOUMI_RACINE:2002} \\
\code{npindex} & Semiparametric Single Index Model & \citet{ICHIMURA:1993}, \citet{KLEIN_SPADY:1993}\\
\code{npindexbw} & Semiparametric Single Index Model Parameter and Bandwidth Selection& \citet{ICHIMURA:1993}, \citet{KLEIN_SPADY:1993}\\
\code{npksum} &  Nonparametric Kernel Sums & \\
\code{npplot} & General Purpose Plotting of Nonparametric Objects& \\
\code{npplreg} & Semiparametric Partially Linear  Regression &
\citet{ROBINSON:1988}, \citet{GAO_LIU_RACINE:2012} \\
\code{npplregbw} & Semiparametric Partially Linear  Regression Bandwidth Selection & \citet{ROBINSON:1988}, \citet{GAO_LIU_RACINE:2012} \\
\code{npqcmstest} &  Parametric Quantile Regression Model Specification Test
& \citet{ZHENG:1998}, \citet{RACINE:2006}\\
\code{npqreg} & Nonparametric Quantile Regression & \citet{LI_RACINE:2008} \\
\code{npreg} &  Nonparametric Regression & \citet{RACINE_LI:2004}, \citet{LI_RACINE:2004}\\
\code{npregbw} &  Nonparametric Regression Bandwidth Selection & \citet{HURVICH_SIMONOFF_TSAI:1998}, \citet{RACINE_LI:2004}, \citet{LI_RACINE:2004} \\
\code{npscoef} & Semiparametric Smooth Coefficient  Regression& \citet{LI_RACINE:2010}\\
\code{npscoefbw} & Semiparametric Smooth Coefficient  Regression Bandwidth Selection& \citet{LI_RACINE:2010}\\
\code{npsdeptest} & Nonparametric Entropy Test for Serial Nonlinear Dependence& \citet{GRANGER_MAASOUMI_RACINE:2004}\\
\code{npsigtest} &  Nonparametric Regression Significance Test & \citet{RACINE:1997}, \citet{RACINE_HART_LI:2006}\\
\code{npsymtest} & Nonparametric Entropy Test for Asymmetry& \citet{MAASOUMI_RACINE:2009}\\
\code{npudens} &  Nonparametric Density Estimation & \citet{PARZEN:1962}, \citet{ROSENBLATT:1956}, \citet{LI_RACINE:2003a}\\
\code{npudensbw} &  Nonparametric Density Bandwidth Selection & \citet{PARZEN:1962}, \citet{ROSENBLATT:1956}, \citet{LI_RACINE:2003a}\\
\code{npudist} &  Nonparametric Distribution Estimation & \citet{PARZEN:1962}, \citet{ROSENBLATT:1956}, \citet{LI_RACINE:2003a}\\
\code{npunitest} &  Nonparametric Entropy Test for Univariate Density Equality & \citet{MAASOUMI_RACINE:2002} \\
-- Utilities --&&\\
\code{gradients} & Extract Gradients& \\
\code{se} &	Extract Standard Errors&\\
\code{uocquantile} & Compute Quantiles/Modes for Unordered, Ordered, and Numeric Data& \\
    \end{tabular}
    \label{np function table}
  }
  \caption{\textbf{\pkg{np} functions.}}
\end{table}
	
In this article, we illustrate the use of the \pkg{np} package via a
number of empirical applications. Each application is chosen to
highlight a specific econometric method in an applied setting. We
begin first with a discussion of some of the features and
implementation details of the \pkg{np} package in
Section~\ref{implementation section}. We then proceed to illustrate
the functionality of the package via a series of applications,
sometimes beginning with a classical parametric method that will
likely be familiar to the reader, and then performing the same
analysis in a semi- or nonparametric framework. It is hoped that such
comparison helps the reader quickly gauge whether or not there is any
value added by moving towards a nonparametric framework for the
application they have at hand. We commence with the workhorse of
applied data analysis (regression) in Section~\ref{regression
  section}, beginning with a simple univariate regression example and
then moving on to a multivariate example. We then proceed to
nonparametric methods for binary and multinomial outcome models in
Section~\ref{count section}. Section~\ref{pdf and cdf section}
considers nonparametric methods for unconditional probability density
function (PDF) and cumulative distribution function (CDF) estimation,
while Section~\ref{cpdf and ccdf section} considers conditional PDF
and CDF estimation, and nonparametric estimators of quantile models
are considered in Section~\ref{quantile section}. A range of
semiparametric models are then considered, including partially linear
models in Section~\ref{partially linear section}, single-index models
in Section~\ref{single index section}, and finally varying coefficient
models are considered in Section~\ref{varying coefficient section}.

\section{Important implementation details}\label{implementation section}

In this section we describe some implementation details that may help
users navigate the methods that reside in the \pkg{np} package. We
shall presume that the user is familiar with the traditional kernel
estimation of, say, density functions (e.g.,
\citet{ROSENBLATT:1956}, \citet{PARZEN:1962}) and regression
functions (e.g., \citet{NADARAYA:1965}, \citet{WATSON:1964})
when the underlying data is continuous in nature. However, we do not
presume familiarity with mixed-data kernel methods hence briefly
describe modifications to the kernel function that are necessary to
handle the mix of categorical and continuous data often encountered in
applied settings. These methods, of course, collapse to the familiar
estimators when all variables are continuous.

\subsection{The primacy of the bandwidth}

Bandwidth selection is a key aspect of sound nonparametric and
semiparametric kernel estimation. It is the direct counterpart of
model selection for parametric approaches, and should therefore not be
taken lightly. \pkg{np} is designed from the ground up to make
bandwidth selection the focus of attention. To this end, one typically
begins by creating a `bandwidth object' which embodies all aspects of
the method, including specific kernel functions, data names,
datatypes, and the like. One then passes these bandwidth objects to
other functions, and those functions can grab the specifics from the
bandwidth object thereby removing potential inconsistencies and
unnecessary repetition. For convenience these steps can be combined
should the user so choose, i.e., if the first step (bandwidth
selection) is not performed explicitly then the second step will
automatically call the omitted first step bandwidth selection using
defaults unless otherwise specified, and the bandwidth object could
then be retrieved retroactively if so desired. Note that the combined
approach would not be a wise choice for certain applications such as
when bootstrapping (as it would involve unnecessary computation since
the bandwidths would properly be those for the original sample and not
the bootstrap resamples) or when conducting quantile regression (as it
would involve unnecessary computation when different quantiles are
computed from the same conditional cumulative distribution estimate).

Work flow therefore typically proceeds as follows:

\begin{enumerate}

\item compute data-driven bandwidths;

\item using the bandwidth object, proceed to estimate a model and
  extract fitted or predicted values, standard errors, etc.;

\item optionally, plot the object.

\end{enumerate}

In order to streamline the creation of a set of complicated graphics
objects, \code{plot} (which calls \code{npplot}) is dynamic; i.e., you
can specify, say, bootstrapped error bounds and the appropriate
routines will be called in real time. Be aware, however, that
bootstrap methods can be computationally demanding hence some plots
may not appear immediately in the graphics window.

\subsection{Data-driven bandwidth selection methods}

We caution the reader that data-driven bandwidth selection methods can
be computationally demanding.  We ought to also point out that
data-driven (i.e., automatic) bandwidth selection procedures are not
guaranteed always to produce good results due to perhaps the presence
of outliers or the rounding/discretization of continuous data, among
others. For this reason, we advise the reader to interrogate their
bandwidth objects with the \code{summary} command which produces a
table of bandwidths for the continuous variables along with a constant
multiple of $\sigma_x n^\alpha$, where $\sigma_x$ is a variable's
standard deviation, $n$ the number of observations, and $\alpha$ a
known constant that depends on the method, kernel order, and number of
continuous variables involved, e.g., $\alpha=-1/5$ for univariate
density estimation with one continuous variable and a second order
kernel. Seasoned practitioners can immediately assess whether
undersmoothing or oversmoothing may be present by examining these
constants, as the appropriate constant (called the `scaling factor')
that is multiplied by $\sigma_x n^\alpha$ often ranges from between
0.5 to 1.5 for some though not all methods, and it is this constant
that is computed and reported by \code{summary}. Also, the admissible
range for the bandwidths for the categorical variables is provided
when \code{summary} is used, which some readers may also find helpful.

We caution users to use multistarting for any serious application
(multistarting refers to restarting numerical search methods from
different initial values to avoid the presence of local minima - the
default is the minimum of the number of variables or 5 and can be
changed via the argument \code{nmulti =}), and do not recommend
overriding default search tolerances (unless increasing \code{nmulti =}
beyond its default value).

We direct the interested reader to the frequently asked questions
document on the author's website
(\url{http://socserv.mcmaster.ca/racine/np_faq.pdf}) for a range of
potentially helpful tips and suggestions surrounding bandwidth
selection and the \pkg{np} package.

\subsection[Interacting with np functions]{Interacting with \pkg{np} functions}

A few words about the \proglang{R} \code{data.frame} construct are in
order. Data frames are fundamental objects in \proglang{R}, defined as
``tightly coupled collections of variables which share many of the
properties of matrices and of lists, used as the fundamental data
structure by most of R's modeling software.'' A data frame is ``a
matrix-like structure whose columns may be of differing types
(numeric, logical, factor and character and so on).'' Seasoned
\proglang{R} users would, prior to estimation or inference, transform
a given data frame into one with appropriately classed elements (the
\pkg{np} package contains a number of datasets whose variables have
already been classed appropriately). It will be seen that appropriate
classing of variables is crucial in order for functions in the
\pkg{np} package to {\em automatically} use appropriate weighting
functions which differ according to a variable's class. If your data
frame contains variables that have not been classed appropriately, you
can do this `on the fly' by re-classing the variable upon invocation
of an \pkg{np} function, however, it is preferable to begin with a
data frame having appropriately classed elements.

There are two ways in which you can interact with functions in
\pkg{np}, namely using data frames or using a formula interface, where
appropriate.  Every function in \pkg{np} supports both interfaces,
where appropriate.

To some, it may be natural to use the data frame interface.  If you
find this most natural for your project, you first create a data frame
casting data according to their type (i.e., one of continuous
(default), \code{factor}, \code{ordered}), as in
\begin{verbatim} 
R> data.object <- data.frame(x1 = factor(x1), x2, x3 = ordered(x3)) 
\end{verbatim} 
where $x_1$ is, say, a binary \code{factor}, $x_2$ continuous, and
$x_3$ an \code{ordered} \code{factor}. Then you could pass this data
frame to the appropriate \pkg{np} function, say
\begin{verbatim} 
R> bw <- npudensbw(dat = data.object)
\end{verbatim}

To others, however, it may be natural to use the formula interface
that is used for the regression example outlined below. For
nonparametric regression functions such as \code{npregbw}, you would
proceed as you might using \code{lm}, e.g.,
\begin{verbatim}
R> bw <- npregbw(y ~ x1 + x2)
\end{verbatim}
except that you would of course not need to specify, e.g., polynomials
in variables, interaction terms, or create a number of dummy variables
for a factor. Of course, if the variables in your data frame have not
been classed appropriately, then you would need to explicitly cast,
say, \code{x1} via \code{npregbw(y ~ factor(x1) + x2)} above.

A few words on the formula interface are in order.  We use the
standard formula interface as it provides capabilities for handling
missing observations and so forth.  This interface, however, is simply
a convenient device for telling a routine which variable is, say, the
outcome and which are, say, the covariates.  That is, just because one
writes $x_1+x_2$ in no way means or is meant to imply that the model
will be linear and additive (why use fully nonparametric methods to
estimate such models in the first place?). It simply means that there
are, say, two covariates in the model, the first being $x_1$ and the
second $x_2$, we are passing them to a routine with the formula
interface, and nothing more is presumed nor implied. This will likely
be obvious to most \proglang{R} users, but we point it out simply to
avoid any potential confusion for those unfamiliar with kernel
smoothing methods.

\subsection{Writing your own functions}

We have tried to make \pkg{np} flexible enough to be of use to a wide
range of users.  All options can be tweaked by the user (kernel
function, kernel order, bandwidth type, estimator type and so forth).
One function, \code{npksum}, allows you to create your own estimators,
tests, etc.  The function \code{npksum} is simply a call to
specialized \proglang{C} code, so you get the benefits of compiled
code along with the power and flexibility of the \proglang{R}
language. We hope that incorporating the \code{npksum} function
renders the package suitable for teaching and research alike.

\subsection{Generalized product kernels}\label{generalized kernel
  section}

As noted above, traditional nonparametric kernel methods presume that
the underlying data is continuous in nature, which is frequently not
the case.  The basic idea underlying the treatment of kernel methods
in the presence of a mix of categorical and continuous data lies in
the use of what we call `generalized product kernels', which we
briefly summarize.

Suppose that you are interested in kernel estimation for `unordered'
categorical data, i.e., you are presented with discrete data $X^d\in
{\mathcal S}^d$, where ${\mathcal S}^d$ denotes the support of $X^d$.
We use $x_s^d$ and $X_{is}^d$ to denote the $s$th component of $x^d$
and $X_i^d$ ($i=1,\dots,n$), respectively.  Following
\citet{AITCHISON_AITKEN:1976}, for $x_s^d$, $X^d_{is}\in \{0,1,\dots,
c_s-1\}$, we define a discrete univariate kernel function
\begin{equation}
  \label{mixed_smooth:eq:l(.)}
  l^u(X^d_{is},x^d_s,\lambda_s) = \left\{ \begin{array}{ll}
      1 - \lambda_s &\text{ if } X^d_{is} = x^d_s \\
      \lambda_s/(c_s-1) &\text{ if } X^d_{is} \neq x^d_s.
    \end{array}
  \right.
\end{equation}

Note that when $\lambda_s = 0$, $l(X^d_{is},x^d_s,0)={\bf
  1}(X_{is}^d=x^d_s)$ becomes an indicator function, and if $\lambda_s
= (c_s - 1)/c_s$, then $l\left(X^d_{is},x^d_s,
  \frac{c_s-1}{c_s}\right) = 1/c_s$ is a constant for {\it all} values
of $X_{is}^d$ and $x^d_s$.  The range of $\lambda_s$ is $[0,
(c_s-1)/c_s]$. Observe that these weights sum to 1.

If, instead, some of the discrete variables are ordered (i.e., are
`ordinal categorical variables'), then we should use a kernel function
which is capable of reflecting their ordered status. Assuming that
$x^d_s$ can take on $c_s$ different ordered values, $\{0,1,\dots
,c_s-1\}$, \citet[p.~29]{AITCHISON_AITKEN:1976} suggested using the
kernel function given by
\begin{equation}
l^o(x^d_{s},v^d_{s},\lambda_s) =
\begin{pmatrix}c_s\cr j\end{pmatrix}
\lambda_s^{j}(1-\lambda_s)^{c_s-j}\text{ when }|x^d_{s}-v^d_s|=j\qquad (0\leq
s\leq c_s),
\end{equation}
 where
\begin{equation}
\begin{pmatrix}
c_s\cr j
\end{pmatrix}
=\frac{c_s!}{j!(c_s-j)!}.
\end{equation}
Observe that these weights sum to 1.

If instead a variable was continuous, you could use, say, the second
order Gaussian kernel, namely
\begin{equation}
w(x^c,X^c_i,h)=\frac{1}{\sqrt{2\pi}}\exp\left\{-\frac{1}{2}\left(\frac{X^c_i-x^c}{h}\right)^2\right\}.
\end{equation}
Observe that these weights integrate to 1.

The `generalized product kernel function' for a vector of, say, one
unordered, one ordered, and one continuous variable is simply the
product of $l^u(\cdot)$, $l^o(\cdot)$, and $w(\cdot)$, and
multivariate versions with differing numbers of each datatype are
created in the same fashion. We naturally allow the bandwidths to
differ for each variable. For what follows, for a vector of mixed
variables, we define $K_{\gamma}(x,X_i)$ to be this product, where
$\gamma$ is the vector of bandwidths (e.g., the $h$s and $\lambda$s)
and $x$ the vector of mixed datatypes.  For further details see
\citet{LI_RACINE:2003a} who proposed the use of these generalized
product kernels for unconditional density estimation and developed the
underlying theory for a data-driven method of bandwidth selection for
this class of estimators.  The use of such kernels offers a seamless
framework for kernel methods with mixed data. For further details on a
range of kernel methods that employ this approach, we direct the
interested reader to \citet[Chapter~4]{LI_RACINE:2007}.

If the user wishes to apply kernel functions other than those provided
by the default settings, the kernel function can be changed by passing
the appropriate arguments to \code{ukertype}, \code{okertype}, and
\code{ckertype} (the first letter of each representing unordered,
ordered, and continuous, respectively), while the `order' for the
continuous kernel (i.e., the first non-zero moment) can be changed by
passing the appropriate (even) integer to \code{ckerorder}. We support
a variety of unordered, ordered, and continuous kernels along with a
variety of high-order kernels, the default being \code{ckerorder =
  2}. Using these arguments, the user could select an eighth order
Epanechnikov kernel, a fourth order Gaussian kernel, or even a uniform
kernel for the continuous variables in their application. See
\code{?np} for further details.

\section{Nonparametric regression}\label{regression section}

We shall start with the workhorse of applied data analysis, namely,
regression models. In order to introduce nonparametric regression, we
shall first consider the simplest possible regression model, one that
involves one continuous dependent variable, $y$, and one continuous
explanatory variable, $x$.  We shall begin with a popular parametric
model of a wage equation, and then move on to a fully nonparametric
regression model. Having compared and contrasted the parametric and
nonparametric approach towards univariate regression, we then proceed
to multivariate regression.

\subsection{Univariate regression}

We begin with a classic dataset taken from
\citet[p.~155]{PAGAN_ULLAH:1999} who consider Canadian cross-section
wage data consisting of a random sample taken from the 1971 Canadian
Census Public Use Tapes for male individuals having common education
(Grade 13). There are $n=205$ observations in total, and 2 variables,
the logarithm of the individual's wage (\code{logwage}) and their age
(\code{age}).  The traditional wage equation is typically modelled as
a quadratic in \code{age}.

First, we begin with a simple parametric model for this relationship.

<<eval=TRUE,keep.source=TRUE>>=
library("np")
data("cps71")
model.par <- lm(logwage ~ age + I(age^2), data = cps71)
summary(model.par)
@ 

If we first consider the model's goodness of fit, the model has an
unadjusted $R^2$ of \Sexpr{summary(model.par)$r.squared}\%.  Next, we
consider the local linear nonparametric method proposed by
\citet{LI_RACINE:2004} employing cross-validated bandwidth selection
using the method of \citet{HURVICH_SIMONOFF_TSAI:1998}. Note that in
this example we conduct bandwidth selection and estimation in one
step.


<<eval=TRUE,keep.source=TRUE>>=
model.np <- npreg(logwage ~ age, 
                  regtype = "ll", 
                  bwmethod = "cv.aic",
                  gradients = TRUE,
                  data = cps71)
summary(model.np)
@ 

Using the measure of goodness of fit introduced in the next section,
we see that this method produces a better in-sample model, at least as
measured by the $R^2$ criterion, having an $R^2$ of
\Sexpr{model.np$R2}\%.\footnote{See Section~\ref{r-squared section}
  for details on computation of the nonparametric $R^2$ measure.}

So far we have summarized the model's goodness-of-fit. However,
econometricians also routinely report the results from tests of
significance. There exist nonparametric counterparts to these tests
that were proposed by \citet{RACINE:1997}, and extended to admit
categorical variables by \citet{RACINE_HART_LI:2006}, which we conduct
below.

<<eval=TRUE,keep.source=TRUE>>=
npsigtest(model.np)
@ 

Having conducted this test we observe that, as was the case for the
linear parametric model, the explanatory variable \code{age} is
significant at all conventional levels in the local linear
nonparametric model.

\subsubsection{Assessing goodness of fit for nonparametric
  models}\label{r-squared section}

The reader may have wondered what the difference is between the $R^2$
measures reported by the linear and nonparametric models summarized
above, or perhaps how the $R^2$ was generated for the nonparametric
model.  It is desirable to use a unit-free measure of goodness-of-fit
for nonparametric regression models which is comparable to that used
for parametric regression models, namely $R^2$. Note that this will be
a {\em within-sample} measure of goodness-of-fit. Given the known
drawbacks of computing $R^2$ based on the decomposition of the sum of
squares (such as possible negative values), there is an alternative
definition and method for computing $R^2$ which can be used that is
directly applicable to any model, linear or nonlinear.  Letting $y_i$
denote the outcome and $\hat y_i$ the fitted value for observation
$i$, we may define $R^2$ as follows:
\begin{equation*}
R^2=\frac{\left[\sum_{i=1}^n(y_i-\bar y)(\hat y_i-\bar y)\right]^2}
{\sum_{i=1}^n(y_i-\bar y)^2\sum_{i=1}^n(\hat y_i-\bar y)^2}
\end{equation*}
and this measure will {\em always} lie in the range $[0,1]$ with the
value 1 denoting a perfect fit to the sample data and 0 denoting no
predictive power above that given by the unconditional mean of the
outcome. It can be demonstrated that this method of computing $R^2$ is
identical to the standard measure computed as $\sum_{i=1}^n(\hat
y_i-\bar y)^2/\sum_{i=1}^n(y_i-\bar y)^2$ {\em when the model is
  linear, fitted with least squares, and includes an intercept
  term}. This useful measure will permit direct comparison of
within-sample goodness-of-fit subject to the obvious qualification
that this is by no means a model selection criterion, rather, simply a
summary measure that some may wish to report. This measure can, of
course, also be computed using out-of-sample predictions and
out-of-sample realizations.  Were we to consider models estimated on a
randomly selected subset of data and evaluated on an independent
sample of hold-out data, this measure computed for the hold-out
observations might serve to guide model selection, particularly when
averaged over a number of independent hold-out datasets.

\subsubsection{Graphical comparison of parametric and nonparametric
  models}

Often, a suitable graphical comparison of models allows the user to
immediately appreciate features present in both the data and the
estimated models.\footnote{By suitable, we mean those that display
  both point estimates and variability estimates.}  The upper left
plot in Figure~\ref{age profile} presents the fitted parametric and
nonparametric regression models along with the data for the
\code{cps71} example. The following code can be used to construct the
plots appearing in Figure~\ref{age profile}.

<<eval=FALSE, keep.source=TRUE>>=
plot(cps71$age, cps71$logwage, xlab = "age", ylab = "log(wage)", cex=.1)
lines(cps71$age, fitted(model.np), lty = 1, col = "blue")
lines(cps71$age, fitted(model.par), lty = 2, col = " red")
plot(model.np, plot.errors.method = "asymptotic")

plot(model.np, gradients = TRUE)
lines(cps71$age, coef(model.par)[2]+2*cps71$age*coef(model.par)[3],
      lty = 2,
      col = "red")
plot(model.np, gradients = TRUE, plot.errors.method = "asymptotic")
@ 

\begin{figure}[!ht]
\begin{center}
<<fig=TRUE, png=TRUE, resolution=100, multifig=TRUE, eval=TRUE, echo=FALSE, keep.source=TRUE, height=8, width=8>>=
options(SweaveHooks = list(multifig = function() par(mfrow=c(2,2),mar=c(4,4,3,2))))

# Plot 1

plot(cps71$age,cps71$logwage,xlab="age",ylab="log(wage)",cex=.1)
lines(cps71$age,fitted(model.np),lty=1,col="blue")
lines(cps71$age,fitted(model.par),lty=2,col="red")

# Plot 2

plot(cps71$age,gradients(model.np),xlab="age",ylab="gradient",type="l",lty=1,col="blue")
lines(cps71$age,coef(model.par)[2]+2*cps71$age*coef(model.par)[3],lty=2,col="red")

# Plot 3

plot(cps71$age,fitted(model.np),xlab="age",ylab="log(wage)",ylim=c(min(fitted(model.np)-2*model.np$merr),max(fitted(model.np)+2*model.np$merr)),type="l")
lines(cps71$age,fitted(model.np)+2*model.np$merr,lty=2,col="red")
lines(cps71$age,fitted(model.np)-2*model.np$merr,lty=2,col="red")

# Plot 4

plot(cps71$age,gradients(model.np),xlab="age",ylab="gradient",ylim=c(min(gradients(model.np)-2*model.np$gerr),max(gradients(model.np)+2*model.np$gerr)),type="l",lty=1,col="blue")
lines(cps71$age,gradients(model.np)+2*model.np$gerr,lty=2,col="red")
lines(cps71$age,gradients(model.np)-2*model.np$gerr,lty=2,col="red")

@ 
\caption{\label{age profile}The figure on the upper left presents the
  parametric (quadratic, dashed line) and the nonparametric estimates
  (solid line) of the regression function for the \code{cps71} data. The
  figure on the upper right presents the parametric (dashed line) and
  nonparametric estimates (solid line) of the gradient. The figures on
  the lower left and lower right present the nonparametric estimates
  of the regression function and gradient along with their variability
  bounds, respectively.}
\end{center}
\end{figure}

The upper right plot in Figure~\ref{age profile} compares the
gradients for the parametric and nonparametric models. Note that the
gradient for the parametric model will be given by $\partial
\text{logwage}_i/\partial\text{age}_i=\partial\hat\beta_2+2\hat\beta_3\text{age}_i$,
$i=1,\dots,n$, as the model is quadratic in age.

Of course, it might be preferable to also plot error bars for the
estimates, either asymptotic or resampled. We have automated this in
the function \code{npplot} which is automatically called by
\code{plot}. The lower left and lower right plots in Figure~\ref{age
  profile} present pointwise error bars using asymptotic standard
error formulas for the regression function and gradient, respectively.

Often, however, distribution free (bootstrapped) error bounds may be
desired, and we allow the user to readily do so as we have written
\pkg{np} to leverage the \pkg{boot} package
(\citeauthor{R-boot-package} \citeyear{R-boot-package}). By default
(`\code{iid}'), bootstrap resampling is conducted pairwise on
$(y,X,Z)$ (i.e., by resampling from rows of the $(y,X)$ data or
$(y,X,Z)$ data where appropriate). Specifying the type of
bootstrapping as `\code{inid}' admits general heteroskedasticity of
unknown form via the wild bootstrap \citep{LIU:1988}, though it does
not allow for dependence. `\code{fixed}' conducts the block bootstrap
\citep{KUNSCH:1989} for dependent data, while `\code{geom}' conducts
the stationary bootstrap \citep{POLITIS_ROMANO:1994}.

\subsubsection{Generating predictions from nonparametric models}

Once you have obtained appropriate bandwidths and estimated a
nonparametric model, generating predictions is straightforward
involving nothing more than creating a set of explanatory variables
for which you wish to generate predictions. These can lie inside the
support of the original data or outside should the user so choose. We
have written our routines to support the \code{predict} function in R,
so by using the \code{newdata =} option one can readily generate
predictions. It is important to note that typically you do not have
the outcome for the evaluation data, hence you need only provide the
explanatory variables.  However, if by chance you do have the outcome
and you provide it, the routine will compute the out-of-sample summary
measures of predictive ability. This would be useful when one splits a
dataset into two independent samples, estimates a model on one sample,
and wishes to assess its performance on the independent hold-out data.

By way of example we consider the \code{cps71} data, generate a set of
values for \code{age}, two of which lie outside of the support of the
data (10 and 70 years of age), and generate the parametric and
nonparametric predictions using the generic \code{predict} function.

<<eval=TRUE,keep.source=TRUE>>=
cps.eval <- data.frame(age = seq(10,70, by=10))
predict(model.par, newdata = cps.eval)
predict(model.np, newdata = cps.eval)
@ 

Note that if you provide \code{predict} with the argument
\code{se.fit = TRUE}, it will also return pointwise asymptotic standard
errors where appropriate.

\subsection{Multivariate regression with qualitative and quantitative
  data}\label{wage1 section}

Based on the presumption that some readers will be unfamiliar with the
kernel smoothing of qualitative data, we next consider a multivariate
regression example that highlights the potential benefits arising from
the use of kernel smoothing methods that smooth both the qualitative
and quantitative variables in a particular manner. For what follows,
we consider an application taken from \citet[p.~226]{WOOLDRIDGE:2003}
that involves multiple regression analysis with qualitative
information.

We consider modeling an hourly wage equation for which the dependent
variable is $\log$(\code{wage}) (\code{lwage}) while the explanatory
variables include three continuous variables, namely \code{educ}
(years of education), \code{exper} (the number of years of potential
experience), and \code{tenure} (the number of years with their current
employer) along with two qualitative variables, \code{female}
(`\code{Female}'/`\code{Male}') and \code{married}
(`\code{Married}'/`\code{Notmarried}').  For this example there are
$n=526$ observations.

The classical parametric approach towards estimating such
relationships requires that one first specify the functional form of
the underlying relationship.  We start by first modelling this
relationship using a simple parametric linear model. By way of
example, \citet[p.~222]{WOOLDRIDGE:2003} presents the following
model:\footnote{We would like to thank Jeffrey Wooldridge for allowing
  us to incorporate his data in the \pkg{np} package. Also, we would
  like to point out that Wooldridge starts out with this na\"ive
  linear model, but quickly moves on to a more realistic model
  involving nonlinearities in the continuous variables and so
  forth. The purpose of this example is simply to demonstrate how
  nonparametric models can outperform misspecified parametric models
  in multivariate finite-sample settings.}
<<eval=TRUE,keep.source=TRUE>>=
data("wage1")
model.ols <- lm(lwage ~ female +
                married +
                educ +
                exper +
                tenure,
                data = wage1)
summary(model.ols)
@

This model is, however, restrictive in a number of ways. First, the
analyst must specify the functional form (in this case linear) for the
continuous variables (\code{educ}, \code{exper}, and \code{tenure}).
Second, the analyst must specify how the qualitative variables
(\code{female} and \code{married}) enter the model (in this case they
affect the model's intercepts only). Third, the analyst must specify
the nature of any interactions among all variables, quantitative and
qualitative (in this case, there are none).  Should any of these
presumptions be incorrect, then the estimated model will be biased and
inconsistent potentially leading to faulty inference.

One might next test the null hypothesis that this parametric linear
model is correctly specified using the consistent model specification
test described in \citet{HSIAO_LI_RACINE:2007} that admits both
categorical and continuous data (we have overridden the default search
tolerances for this example as the objective function is well-behaved
via \code{tol = 0.1} and \code{ftol = 0.1}, which should {\em not} be done
in general - this example will likely take a few minutes on a desktop
computer as it uses bootstrapping and cross-validated bandwidth
selection):
<<eval=TRUE,keep.source=TRUE>>=
model.ols <- lm(lwage ~ female +
                married +
                educ +
                exper +
                tenure,
                x = TRUE,
                y = TRUE,
                data = wage1)
X <- data.frame(wage1$female,
                wage1$married,
                wage1$educ,
                wage1$exper,
                wage1$tenure)
output <- npcmstest(model = model.ols, 
                    xdat = X, 
                    ydat = wage1$lwage, 
                    nmulti = 1,
                    tol = 0.1, 
                    ftol = 0.1)
summary(output)
@

Note that it might appear at first blush that the user needs to do
some redundant typing as the $X$ data in this example is the same as
the regressors in the model. However, in general $X$ could differ
which is why the user must specify this object separately as we cannot
assume that the explanatory variables in the model will be equal to
$X$.

This na\"ive linear model is rejected by the data (the $p$-value for
the null of correct specification is $<0.001$), hence one might
proceed instead to model this relationship using kernel methods.

As noted, the traditional nonparametric approach towards modeling
relationships in the presence of qualitative variables requires that
you first split your data into subsets containing only the continuous
variables of interest (\code{lwage}, \code{exper}, and \code{tenure}).
For instance, we would have four such subsets, a) $n=132$ observations
for married females, b) $n=120$ observations for single females, c)
$n=86$ observations for single males, and d) $n=188$ observations for
married males. One would then construct smooth nonparametric
regression models for each of these subsets and proceed with analysis
in this fashion. However, this may lead to a loss in efficiency due to
a reduction in the sample size leading to overly variable estimates of
the underlying relationship.

Instead, however, we could construct smooth nonparametric regression
models by i) using a kernel function that is appropriate for the
qualitative variables as outlined in Section~\ref{generalized kernel
  section} and ii) modifying the nonparametric regression model as was
done by \citet{LI_RACINE:2004}. One can then conduct sound
nonparametric estimation based on the $n=526$ observations rather than
resorting to sample splitting. The rationale for this lies in the fact
that doing so may introduce potential bias, however it will always
reduce variability thus leading to potential finite-sample efficiency
gains. Our experience has been that the potential benefits arising
from this approach more than offset the potential costs in
finite-sample settings.

Next, we consider using the local-linear nonparametric method
described in \citet{LI_RACINE:2004}. For the reader's convenience we
supply precomputed cross-validated bandwidths which are automatically
loaded when one loads the \code{wage1} dataset (recall being cautioned
about the computational burden associated with multivariate
data-driven bandwidth methods).

% The commented out code that generates the cross-validated bandwidths
% is also provided should the reader wish to fully replicate the
% example

In the example that follows, we use the precomputed bandwidth object
\code{bw.all} that contains the data-driven bandwidths for the local
linear regression produced below using all observations in the sample.

<<eval=TRUE,keep.source=TRUE>>=
#bw.all <- npregbw(formula = lwage ~ female +
#                  married +
#                  educ +
#                  exper +
#                  tenure,
#                  regtype = "ll",
#                  bwmethod = "cv.aic",
#                  data = wage1)
model.np <- npreg(bws = bw.all)
summary(model.np)
@

Note again that the bandwidth object is the only thing you need to
pass to \code{npreg} as it encapsulates the kernel types, regression
method, and so forth. You could also use \code{npreg} and manually
specify the bandwidths using a bandwidth vector if you so
choose.\footnote{For example, attach a dataset via \code{data(cps71)}
  then \code{attach(cps71)} then enter, say, \code{plot(age,logwage)}
  and \code{lines(age,fitted(npreg(logwage~age,bws=2)))} to plot the
  local constant estimator with a bandwidth of 2 years).} We have
tried to make each function as flexible as possible to meet the needs
of a variety of users.

The goodness of fit of the nonparametric model ($R^2=51.5\%$) is
better than that for the parametric model ($R^2=40.4\%$).  In order to
investigate whether this apparent improvement reflects overfitting or
simply that the nonparametric model is in fact more faithful to the
underlying data generating process, we shuffled the data and created
two independent samples, one of size $n_1=400$ and one of size
$n_2=126$. We fit the models on the $n_1$ training observations then
evaluate the models on the $n_2$ independent hold-out observations
using the predicted square error criterion, namely
$n_2^{-1}\sum_{i=1}^{n_2}(y_i-\hat y_i)^2$, where the $y_i$s are the
\code{lwage} values for the hold-out observations and the $\hat y_i$s
are the predicted values. Finally, we compare the parametric model,
the nonparametric model that smooths both the categorical and
continuous variables, and the traditional frequency nonparametric
model that breaks the data into subsets and smooths the continuous
data only. For this example we use the precomputed bandwidth object
\code{bw.subset} which contains the data-driven bandwidths for a
random subset of data.

<<eval=TRUE,keep.source=TRUE>>=
set.seed(123)
ii <- sample(seq(1, nrow(wage1)), replace=FALSE)
wage1.train <- wage1[ii[1:400],]
wage1.eval <- wage1[ii[401:nrow(wage1)],]
model.ols <- lm(lwage ~ female +
                married +
                educ +
                exper +
                tenure,
                data = wage1.train)
fit.ols <- predict(model.ols,
                   data = wage1.train,
                   newdata = wage1.eval)
pse.ols <- mean((wage1.eval$lwage - fit.ols)^2)
#bw.subset <- npregbw(formula = lwage ~ female +
#                     married +
#                     educ +
#                     exper +
#                     tenure,
#                     regtype = "ll",
#                     bwmethod = "cv.aic",
#                     data = wage1.train)
model.np <- npreg(bws = bw.subset)
fit.np <- predict(model.np,
                  data = wage1.train,
                  newdata = wage1.eval)
pse.np <- mean((wage1.eval$lwage - fit.np)^2)
bw.freq <- bw.subset
bw.freq$bw[1] <- 0
bw.freq$bw[2] <- 0
model.np.freq <- npreg(bws = bw.freq)
fit.np.freq <- predict(model.np.freq,
                       data = wage1.train,
                       newdata = wage1.eval)
pse.np.freq <- mean((wage1.eval$lwage - fit.np.freq)^2)
@

The predicted square error on the hold-out data was
\Sexpr{formatC(pse.ols,format="f",digits=3)} for the parametric linear
model, \Sexpr{formatC(pse.np.freq,format="f",digits=3)} for the
traditional nonparametric estimator that splits the data into subsets,
and \Sexpr{formatC(pse.np,format="f",digits=3)} for the nonparametric
estimator that uses the full sample but smooths both the qualitative
and quantitative data. The nonparametric model that smooths both the
quantitative and qualitative data is more than
\Sexpr{formatC(((pse.ols-pse.np)/pse.ols)*100,format="f",digits=1)}\%
more efficient in terms of out-of-sample predictive ability than the
parametric or frequency nonparametric model, and therefore appears to
provide a better description of the underlying data generating process
than either.

Note that for this example we have only four cells. If one used all
qualitative and binary variables included in the dataset (sixteen in
total), one would have $65,536$ cells, many of which would be empty,
and most having far too few observations to provide meaningful
nonparametric estimates.  As the number of qualitative variables
increases, the difference between the estimator that smooths both
continuous and discrete variables in a particular manner and the
traditional estimator that relies upon sample splitting will become
even more pronounced.

Next, we display partial regression plots in Figure~\ref{wage1
  profile}.\footnote{A `partial regression plot' is simply a 2D plot
  of the outcome $y$ versus one covariate $x_j$ when all other
  covariates are held constant at their respective medians/modes (this
  can be changed by the user).} We also plot bootstrapped variability
bounds, where the bootstrapping is done via the \pkg{boot} package
thereby facilitating a variety of bootstrap methods. The following
code will generate Figure~\ref{wage1 profile}.

<<eval=FALSE, keep.source=TRUE>>=
plot(model.np,
     plot.errors.method = "bootstrap",
     plot.errors.boot.num = 25)
@

\begin{figure}[!ht]
\begin{center}
<<fig=TRUE,eval=TRUE,echo=FALSE,keep.source=TRUE>>=
plot(model.np,
     plot.errors.method = "bootstrap",
     plot.errors.boot.num = 25)
@
\caption{\label{wage1 profile}Partial local linear nonparametric
  regression plots with bootstrapped pointwise error bounds for the
  \code{wage1} dataset.}
\end{center}
\end{figure}

Figure~\ref{wage1 profile} reveals that (holding other regressors
constant at their median/mode), males have higher expected wages than
females, there does not appear to be a significant difference between
the expected wages of married and nonmarried individuals, wages
increase with education and tenure and first rise then fall as
experience increases, other things equal.

\section{Nonparametric binary outcome and count data
  models}\label{count section}

For what follows, we adopt the conditional probability estimator
proposed in \citet{HALL_RACINE_LI:2004} to estimate a nonparametric
model of a binary outcome when there exist a number of categorical
covariates.

For this example, we use the \code{birthwt} data taken from the
\pkg{MASS} package, and compute a parametric Logit model and a
nonparametric conditional mode model. We then compare their confusion
matrices\footnote{A `confusion matrix' is simply a tabulation of the
  actual outcomes versus those predicted by a model. The diagonal
  elements contain correctly predicted outcomes while the off-diagonal
  ones contain incorrectly predicted (confused) outcomes.} and assess
their classification ability. Note that many of the variables in this
dataset have not been classed so we must do this upon invocation of a
function. The outcome is an indicator of low infant birthweight (0/1).
The method can handle unordered and ordered multinomial outcomes
without modification (we have overridden the default search tolerances
for this example as the objective function is well-behaved via
\code{tol = 0.1} and \code{ftol = 0.1}, which should {\em not} be done in
general).  In this example we first model this relationship using a
simple parametric Logit model, then we model this with a nonparametric
conditional density estimator and compute the conditional mode, and
finally, we compare confusion matrices from the logit and
nonparametric models. Note that for the nonparametric estimator we
conduct bandwidth selection and estimation in one step, and we first
convert the data frame to one with appropriately classed elements.

<<eval=TRUE,keep.source=TRUE>>=
data("birthwt", package = "MASS")
birthwt$low <- factor(birthwt$low)
birthwt$smoke <- factor(birthwt$smoke)
birthwt$race <- factor(birthwt$race)
birthwt$ht <- factor(birthwt$ht)
birthwt$ui <- factor(birthwt$ui)
birthwt$ftv <- factor(birthwt$ftv)
model.logit <- glm(low ~ smoke +
                   race +
                   ht +
                   ui +
                   ftv +
                   age +
                   lwt, 
                   family = binomial(link = logit),
                   data = birthwt)
model.np <- npconmode(low ~
                      smoke +
                      race +
                      ht +
                      ui +
                      ftv +
                      age +
                      lwt,
                      tol = 0.1,
                      ftol = 0.1,
                      data = birthwt)
cm <- table(birthwt$low, 
            ifelse(fitted(model.logit) > 0.5, 1, 0))
cm
summary(model.np)
@

For this example the nonparametric model is better able to predict low
birthweight infants than its parametric counterpart, correctly
predicting
\Sexpr{sum(diag(model.np$confusion.matrix))}/\Sexpr{sum(model.np$confusion.matrix)}
birthweights compared with \Sexpr{sum(diag(cm))}/\Sexpr{sum(cm)} for
the parametric model.

\section{Nonparametric unconditional PDF and CDF estimation}\label{pdf
  and cdf section}

The Old Faithful Geyser is a tourist attraction located in Yellowstone
National Park. This famous dataset containing $n=272$ observations
consists of two variables, eruption duration in minutes
(\code{eruptions}) and waiting time until the next eruption in minutes
(\code{waiting}). This dataset is used by the park service to model,
among other things, expected duration conditional upon the amount of
time that has elapsed since the previous eruption. Modeling the joint
distribution is, however, of interest in its own right, and the
underlying bimodal nature of the joint PDF and CDF is readily revealed
by the kernel estimator.  For this example, we load the old faithful
geyser data and compute the density and distribution
functions. Results are presented in Figure~\ref{faithful plot}. Note
that in this example we conduct bandwidth selection and estimation in
one step.

<<eval=TRUE,keep.source=TRUE>>=
data("faithful", package = "datasets")
f.faithful <- npudens(~ eruptions + waiting, data = faithful)
F.faithful <- npudist(~ eruptions + waiting, data = faithful)
summary(f.faithful)
summary(F.faithful)
@

The following code will generate Figure~\ref{faithful plot}.

<<eval=FALSE, keep.source=TRUE>>=
plot(f.faithful, xtrim = -0.2, view = "fixed", main = "")
plot(F.faithful, xtrim = -0.2, view = "fixed", main = "")
@

\begin{figure}[!ht]
\begin{center}
<<fig=TRUE, png=TRUE, resolution=100, multifig=TRUE, eval=TRUE, echo=FALSE, keep.source=TRUE, height=4, width=8>>=
options(SweaveHooks = list(multifig = function() par(mfrow=c(1,2),mar=rep(1.5,4))))
plot.output <- plot(f.faithful, xtrim=-0.2, view="fixed", main = "",plot.behavior="data")
# Retrieve data from plot() to create multiple figures
f <- matrix(plot.output$d1$dens, 50, 50)
plot.x1 <- unique(plot.output$d1$eval[,1])
plot.x2 <- unique(plot.output$d1$eval[,2])
persp(plot.x1,plot.x2,f,xlab="eruptions",ylab="waiting",zlab="Joint Density",col="lightblue", ticktype="detailed")

plot.output <- plot(F.faithful, xtrim = -0.2, view = "fixed", main="",plot.behavior="data")
# Retrieve data from plot() to create multiple figures
F <- matrix(plot.output$d1$dist, 50, 50)
plot.x1 <- unique(plot.output$d1$eval[,1])
plot.x2 <- unique(plot.output$d1$eval[,2])
persp(plot.x1,plot.x2,F,xlab="eruptions",ylab="waiting",zlab="Joint Distribution",col="lightblue", ticktype="detailed")
@
\caption{\label{faithful plot}Nonparametric multivariate PDF and CDF
  estimates for the Old Faithful data.}
\end{center}
\end{figure}

If one were to instead model this density with a parametric model such
as the bivariate normal (being symmetric, unimodal, and monotonically
decreasing away from the mode), one would of course fail to uncover
the underlying structure readily revealed by the kernel estimate.

\section{Nonparametric conditional PDF and CDF estimation}\label{cpdf
  and ccdf section}

We consider Giovanni Baiocchi's (\citeauthor{BAIOCCHI:2006}
\citeyear{BAIOCCHI:2006}) Italian GDP growth panel for 21 regions
covering the period 1951-1998 (millions of Lire, 1990=base).  There
are $n=1,008$ observations in total, and two variables, \code{gdp} and
\code{year}.  First, we compute the bandwidths. Note that this may
take a minute or two depending on the speed of your computer. We
override the default tolerances for the search method as the objective
function is well-behaved (do not of course do this in general), then
we compute the \code{npcdens} object. Note that in this
example we conduct bandwidth selection and estimation in one step.

<<eval=TRUE,keep.source=TRUE>>=
data("Italy")
fhat <- npcdens(gdp ~ year,
                tol = 0.1,
                ftol = 0.1,
                data = Italy)
summary(fhat)
Fhat <- npcdist(gdp ~ year,
                tol = 0.1,
                ftol = 0.1,
                data = Italy)
summary(Fhat)
@ 

Figure~\ref{italy conditional plot} plots the resulting conditional
PDF and CDF for the \code{Italy} GDP panel. The following code will
generate Figure~\ref{italy conditional plot}.

<<eval=FALSE, keep.source=TRUE>>=
plot(fhat, view = "fixed", main = "", theta = 300, phi = 50)
plot(Fhat, view = "fixed", main = "", theta = 300, phi = 50)
@

\begin{figure}[!ht]
\begin{center}
<<fig=TRUE, png=TRUE, resolution=100, multifig=TRUE, eval=TRUE, echo=FALSE, keep.source=TRUE, height=4, width=8>>=
options(SweaveHooks = list(multifig = function() par(mfrow=c(1,2),mar=rep(1.25,4))))
plot.output <- plot(fhat, view="fixed", main="",plot.behavior="data")
# Retrieve data from plot() to create multiple figures
f <- matrix(plot.output$cd1$condens, 48, 50)
plot.y1 <- unique(plot.output$cd1$yeval)
plot.x1 <- unique(plot.output$cd1$xeval)
persp(as.integer(levels(plot.x1)),plot.y1,f,xlab="year",ylab="gdp",zlab="Conditional Density",col="lightblue", ticktype="detailed",theta=300,phi=50)

plot.output <- plot(Fhat, view="fixed", main="",plot.behavior="data")
# Retrieve data from plot() to create multiple figures
F <- matrix(plot.output$cd1$condist, 48, 50)
plot.y1 <- unique(plot.output$cd1$yeval)
plot.x1 <- unique(plot.output$cd1$xeval)
persp(as.numeric(plot.x1)+1951,plot.y1,F,xlab="year",ylab="gdp",zlab="Conditional Distribution",col="lightblue", ticktype="detailed",theta=300,phi=50)

@
\caption{\label{italy conditional plot}Nonparametric conditional PDF
  and CDF estimates for the Italian GDP panel.}
\end{center}
\end{figure}

Figure~\ref{italy conditional plot} reveals that the distribution of
income has evolved from a unimodal one in the early 1950s to a
markedly bimodal one in the 1990s. This result is robust to bandwidth
choice, and is observed whether using simple rules-of-thumb or
data-driven methods such as likelihood cross-validation.  The kernel
method readily reveals this evolution which might easily be missed
were one to use parametric models of the income distribution (e.g.,
the unimodal lognormal distribution is commonly used to model income
distributions).

\section{Nonparametric quantile regression}\label{quantile section}

We again consider Giovanni Baiocchi's Italian GDP growth panel.
First, we compute the likelihood cross-validation bandwidths
(default). We override the default tolerances for the search method as
the objective function is well-behaved (do not of course do this in
general). Then we compute the resulting conditional quantile estimates
using the method of \citet{LI_RACINE:2008}. By way of example, we
compute the 25th, 50th, and 75th conditional quantiles. Note that this
may take a minute or two depending on the speed of your computer.
Note that for this example we first call \code{npcdistbw} to avoid
unnecessary re-computation of the bandwidth object.

<<eval=TRUE,keep.source=TRUE>>=
bw <- npcdistbw(formula = gdp ~ year,
                tol = 0.1,
                ftol = 0.1,
                data = Italy)
model.q0.25 <- npqreg(bws = bw, tau = 0.25)
model.q0.50 <- npqreg(bws = bw, tau = 0.50)
model.q0.75 <- npqreg(bws = bw, tau = 0.75)
@ 

Figure~\ref{italy quantile plot} plots the resulting quantile
estimates. The following code will generate Figure~\ref{italy quantile
  plot}.

<<eval=FALSE,keep.source=TRUE>>=
plot(Italy$year, Italy$gdp, main = "", 
     xlab = "Year", ylab = "GDP Quantiles")
lines(Italy$year, model.q0.25$quantile, col = "red", lty = 1, lwd = 2)
lines(Italy$year, model.q0.50$quantile, col = "blue", lty = 2, lwd = 2)
lines(Italy$year, model.q0.75$quantile, col = "red", lty = 3, lwd = 2)
legend(ordered(1951), 32, c("tau = 0.25", "tau = 0.50", "tau = 0.75"), 
       lty = c(1, 2, 3), col = c("red", "blue", "red"))
@

%% Reset sizing to admit smaller graphs

\setkeys{Gin}{width=0.75\textwidth}

\begin{figure}[!ht]
\begin{center}
<<fig=TRUE,eval=TRUE,echo=FALSE,keep.source=TRUE,width=6,height=5>>=
plot(Italy$year, Italy$gdp, 
     main = "",
     xlab = "Year", 
     ylab = "GDP Quantiles")
lines(Italy$year, model.q0.25$quantile, col = "red", lty = 1, lwd = 2)
lines(Italy$year, model.q0.50$quantile, col = "blue", lty = 2, lwd = 2)
lines(Italy$year, model.q0.75$quantile, col = "red", lty = 3, lwd = 2)
legend(ordered(1951), 32, c("tau = 0.25", "tau = 0.50", "tau = 0.75"), 
       lty = c(1, 2, 3), col = c("red", "blue", "red"))
@
\caption{\label{italy quantile plot}Nonparametric quantile regression
  on the Italian GDP panel.}
\end{center}
\end{figure}

One nice feature of this application is that the explanatory variable
is ordered and there exist multiple observations per year. Using the
\code{plot} function with ordered data produces a boxplot which
readily reveals the non-smooth 25th, 50th, and 75th quantiles. These
non-smooth quantile estimates can then be directly compared to those
obtained via direct estimation of the smooth CDF which are plotted in
Figure~\ref{italy quantile plot}.

\section{Semiparametric partially linear models}\label{partially
  linear section}

Suppose that we consider the \code{wage1} dataset from
\citet[p.~222]{WOOLDRIDGE:2003}, but now assume that the researcher
is unwilling to presume the nature of the relationship between
\code{exper} and \code{lwage}, hence relegates \code{exper} to the
nonparametric part of a semiparametric partially linear model.  The
partially linear model was proposed by \citet{ROBINSON:1988} and
extended to handle the presence of categorical covariates by
\citet{GAO_LIU_RACINE:2012}.

Before proceeding, we ought to clarify a common misunderstanding about
partially linear models. Many believe that, as the model is apparently
simple, its computation ought to also be simple. However, the apparent
simplicity hides the perhaps under-appreciated fact that bandwidth
selection for partially linear models can be orders of magnitude more
computationally burdensome than that for fully nonparametric models,
for one simple reason. Data-driven bandwidth selection methods such as
cross-validation are being used, and the partially linear model
involves cross-validation to regress $y$ on $Z$ ($Z$ is multivariate)
then {\em each column of $X$} on $Z$, whereas fully nonparametric
regression involves cross-validation of $y$ on $X$ only. The
computational burden associated with partially linear models is
therefore much more demanding than for nonparametric models, so be
forewarned. Note that in this example we conduct bandwidth selection
and estimation in one step.

<<eval=TRUE,keep.source=TRUE>>=
model.pl <- npplreg(lwage ~ female +
                    married +
                    educ +
                    tenure | exper,
                    data = wage1)
summary(model.pl)
@ 

A comparison of this model with the parametric and nonparametric
models presented in Section~\ref{wage1 section} indicates an in-sample
fit ($44.9\%$) that lies in between the misspecified parametric model
($40.4\%$) and the fully nonparametric model ($51.5\%$).

\section{Semiparametric single-index models}\label{single index section}

\subsection{Binary outcomes (Klein-Spady with cross-validation)}

We could again consider the \code{birthwt} data taken from the
\pkg{MASS} package, and this time compute a semiparametric index
model. We then compare confusion matrices and assess classification
ability. The outcome is an indicator of low infant birthweight
(0/1). We apply the method of \citet{KLEIN_SPADY:1993} with bandwidths
selected via cross-validation. Note that for this example we conduct
bandwidth selection and estimation in one step

<<eval=TRUE,keep.source=TRUE>>=
model.index <- npindex(low ~
                       smoke +
                       race +
                       ht +
                       ui +
                       ftv +
                       age +
                       lwt,
                       method = "kleinspady",
                       gradients = TRUE,
                       data = birthwt)
summary(model.index)
@ 

It is interesting to compare this with the parametric Logit model's
confusion matrix presented in Section~\ref{count section}.  A
comparison of this model with the parametric model presented in
Section~\ref{count section} reveals that it correctly classifies an
additional 5/189 observations.

\subsection{Continuous outcomes (Ichimura with cross-validation)}

Next, we consider applying \citet{ICHIMURA:1993}'s single-index method
which is appropriate for continuous outcomes, unlike that of
\citet{KLEIN_SPADY:1993} (we override the default number of
multistarts for the user's convenience as the global minimum appears
to have been located in the first attempt). We again consider the
\code{wage1} dataset found in \citet[p.~222]{WOOLDRIDGE:2003}.  Note
that in this example we conduct bandwidth selection and estimation in
one step.

<<eval=TRUE,keep.source=TRUE>>=
model <- npindex(lwage ~ female +
                 married +
                 educ +
                 exper +
                 tenure,
                 data = wage1,
                 nmulti = 1)
summary(model)
@ 

It is interesting to compare this model with the parametric and
nonparametric models presented in Section~\ref{wage1 section} as it
provides an in-sample fit ($43.1\%$) that lies in between the
misspecified parametric model ($40.4\%$) and fully nonparametric model
($51.5\%$). Whether this model yields improved out-of-sample
predictions could also be explored.

\section{Semiparametric varying coefficient models}\label{varying
  coefficient section}

We revisit the \code{wage1} dataset found in
\citet[p.~222]{WOOLDRIDGE:2003}, but assume that the researcher
believes that the model's parameters may differ depending on one's
sex. In this case, one might adopt a varying coefficient approach such
as that found in \citet{LI_RACINE:2010} and
\citet{LI_OUYANG_RACINE:2012}. We compare a simple linear model with
the semiparametric varying coefficient model.  Note that the $X$ data
in the varying coefficient model must be of type \code{numeric}, so we
create a 0/1 dummy variable from the qualitative variable for $X$, but
of course for the nonparametric component we can simply treat these as
unordered factors.  Note that we do bandwidth selection and estimation
in one step.

<<eval=TRUE,keep.source=TRUE>>=
model.ols <- lm(lwage ~ female +
                married +
                educ +
                exper +
                tenure,
                data = wage1)
wage1.augmented <- wage1
wage1.augmented$dfemale <- as.integer(wage1$female == "Male")
wage1.augmented$dmarried <- as.integer(wage1$married == "Notmarried")
model.scoef <- npscoef(lwage ~ dfemale +
                       dmarried +
                       educ +
                       exper +
                       tenure | female,
                       betas = TRUE,
                       data = wage1.augmented)
summary(model.scoef)
colMeans(coef(model.scoef))
coef(model.ols)
@

It is again interesting to compare this model with the parametric and
nonparametric models presented in Section~\ref{wage1 section}. It can be
seen that the {\em average} values of the model's coefficients are in
agreement with those from the linear specification, while the
in-sample goodness of fit ($42.6\%$) lies in between the misspecified
parametric model ($40.4\%$) and fully nonparametric model ($51.5\%$).

\section{Writing your own kernel-based functions}

The function \code{npksum} exists so that you can create your own
kernel objects with or without a variable to be weighted (defaults to
$1$). With the options available, you could create new nonparametric
tests or even new kernel estimators. The convolution kernel option
would allow you to create, say, the least squares cross-validation
function for kernel density estimation.  \code{npksum} implements a
variety of methods for computing multivariate kernel sums
($p$-variate) defined over a set of possibly continuous and/or
discrete data.

By way of example, we construct a local constant kernel estimator with
a bandwidth of, say, two years. Figure~\ref{lc npksum profile} plots
the resulting estimate.

<<eval=TRUE,keep.source=TRUE>>=
fit.lc <- npksum(txdat = cps71$age, tydat = cps71$logwage, bws = 2)$ksum/
  npksum(txdat = cps71$age, bws = 2)$ksum
@ 

The following code will generate Figure~\ref{lc npksum profile}.

<<eval=FALSE, keep.source=TRUE>>=
plot(cps71$age, cps71$logwage, xlab = "Age", ylab = "log(wage)")
lines(cps71$age, fit.lc, col = "blue")
@ 

\begin{figure}[!ht]
\begin{center}
<<fig=TRUE,eval=TRUE,echo=FALSE,keep.source=TRUE>>=
plot(cps71$age, cps71$logwage, xlab = "Age", ylab = "log(wage)", cex=.1)
lines(cps71$age, fit.lc, col = "blue")
@ 
\caption{\label{lc npksum profile}A local constant kernel estimator
  generated with \code{npksum} for the \code{cps71} dataset.}
\end{center}
\end{figure}

\code{npksum} is exceedingly flexible, allowing for leave-one-out
sums, weighted sums of matrices, raising the kernel function to
different powers, the use of convolution kernels, and so forth. See
\code{?npksum} for further details.

\section{A parallel implementation}

Data-driven bandwidth selection methods are, by their nature,
computationally burdensome. However, many bandwidth selection methods
lend themselves well to parallel computing approaches. High
performance computing resources are becoming widely available, and
multiple CPU desktop systems have become the norm and will continue to
make inroads.

When users have a large amount of data, serial bandwidth selection
procedures can be infeasible. For this reason, we have developed an
\proglang{MPI}-aware version of the \pkg{np} package that uses some of
the functionality of the \pkg{Rmpi} package which we have tentatively
called the \pkg{npRmpi} package and is available from the authors upon
request (the Message Passing Interface (MPI) is an open library
specification for parallel computation available for a range of
computing platforms).  The functionality of \pkg{np} and \pkg{npRmpi}
is identical, however, using \pkg{npRmpi} you could take advantage of
a cluster computing environment or a multi-core/multi-CPU desktop
machine thereby alleviating the computational burden associated with
the nonparametric analysis of large datasets. Installation of this
package, however, requires knowledge that goes beyond that which even
seasoned \proglang{R} users may possess.  Having access to local
expertise would be necessary for many users therefore this package is
not available via CRAN.

\section{Summary}

The \pkg{np} package offers users of \proglang{R} a variety of
nonparametric and semiparametric kernel-based methods that are capable
of handling the mix of categorical and continuous data typically
encountered by applied researchers. In this article we have described
the functionality of the \pkg{np} package via a series of illustrative
applications that may be of interest to applied econometricians
interested in becoming familiar with these methods. We do not delve
into details of the underlying estimators, rather we provide
references where appropriate and direct the interested reader to those
resources.

The help files accompanying many functions found in the \pkg{np}
package contain numerous examples which may be of interest to some
readers, and we encourage readers to experiment with these examples in
addition to those contained herein.

Finally, we encourage readers who have successfully implemented new
kernel-based methods using the \code{npksum} function to send such
functions to us so that they can be included in future versions of the
\pkg{np} package with appropriate acknowledgment of course.

\section*{Acknowledgments}

We are indebted to Achim Zeileis and to two anonymous referees whose
comments and feedback have resulted in numerous improvements to the
\pkg{np} package and to the exposition of this article. Racine would
like to gratefully acknowledge support from the Natural Sciences and
Engineering Research Council of Canada (\url{http://www.nserc.ca}),
the Social Sciences and Humanities Research Council of Canada
(\url{http://www.sshrc.ca}), and the Shared Hierarchical Academic
Research Computing Network (\url{http://www.sharcnet.ca}).

\bibliography{np}

\end{document}
