%\VignetteIndexEntry{Maximum Entropy Bootstrap for Time Series: The meboot R Package}

\documentclass[nojss]{jss}

\usepackage{multirow,thumbpdf}

\author{Hrishikesh D. Vinod\\Fordham University
   \And Javier L\'opez-de-Lacalle\\Universidad del Pa\'is Vasco}
\Plainauthor{Hrishikesh D. Vinod, Javier L\'opez-de-Lacalle}

\title{Maximum Entropy Bootstrap for Time Series: The \pkg{meboot} \proglang{R} Package}
\Plaintitle{Maximum Entropy Bootstrap for Time Series: The meboot R Package}
\Shorttitle{\pkg{meboot}: Maximum Entropy Bootstrap for Time Series}

\Abstract{
  This introduction to the \proglang{R} package \pkg{meboot} is a (slightly)
  modified version of \cite{Vinod+Lopez-de-Lacalle:2009}, published in the
  \emph{Journal of Statistical Software}.

  The maximum entropy bootstrap is an algorithm that creates an ensemble for time series
  inference. Stationarity is not required and the ensemble satisfies the ergodic theorem and
  the central limit theorem. The \pkg{meboot} \proglang{R} package implements such algorithm.
  This document introduces the procedure and illustrates its scope by means
  of several guided applications.
}

\Keywords{time series, dependent data, bootstrap, \proglang{R}}
\Plainkeywords{time series, dependent data, bootstrap, R}

\Address{
  Hrishikesh D. Vinod\\
  Fordham University\\
  Department of Economics, Institute of Ethics and Economic Policy\\
  Bronx, NY 10458, United States of America\\
  E-mail: \email{Vinod@fordham.edu}\\

  Javier L\'opez-de-Lacalle\\
  Universidad del Pa\'is Vasco\\
  Facultad de Ciencias Econ\'omicas y Empresariales\\
  48015 Bilbao, Spain\\
  E-mail: \email{javlacalle@yahoo.es}\\
}

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@
%\VignetteIndexEntry{Maximum Entropy Bootstrap for Time Series: The meboot R Package}
%\VignetteDepends{meboot,car,lmtest,dynlm,boot,hdrcde,plm}
%\VignetteKeywords{time series, dependent data, bootstrap, R}
%\VignettePackage{meboot}

\begin{document}




%%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%

\section{Introduction}

%%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%

This paper illustrates the use of the \pkg{meboot} \proglang{R} package for \proglang{R}
\citep{Rproject}.
The package \pkg{meboot} implements the maximum entropy bootstrap algorithm for
time series described in \citet{Vinod:04, Vinod:06}. The package can be obtained
from the  Comprehensive \proglang{R} Archive Network at
\url{https://CRAN.R-project.org/package=meboot}.

In  the traditional theory, an ensemble $\Omega$
represents the population from which the observed time series is drawn. The maximum
entropy (ME) bootstrap constructs a large number of replicates ($J=999$, say) as
elements of $\Omega$ for inference using a seven-step algorithm designed to satisfy
the  ergodic theorem (the grand mean of all ensembles is close to the sample mean).
The algorithm's practical appeal is that it avoids all structural change and unit root
type testing involving complicated asymptotics and all shape-destroying transformations
like de-trending or differencing to achieve stationarity. The constructed
ensemble elements retain the basic shape and time dependence structure of the autocorrelation
function (ACF) and the partial autocorrelation function (PACF) of the original time series.

This discussion collects relevant portions of \citet{Vinod:04, Vinod:06} as templates for
users of the \pkg{meboot} package.  Let us begin with some motivation. Wiener, Kolmogorov
and Khintchine \citep[WKK,][]{Wiener:30, Kolmogorov:31, Khintchine:34}, among others,
developed the stationary model in the 1930's where the data
$x_t$ arise from the $\Omega$ mentioned above.
Stationary time series are integrated of
order zero, I(0).  Many real world applications involve a mixture of I(0) and
nonstationary I($d$) series, where the order of integration d can be different for
different series and even fractional, and where the stationarity assumptions are
difficult to verify. The situation is much worse in the presence of regime switching
structural changes and other jump discontinuities occurring
at arbitrary times. The WKK theory mostly needs the zero memory I(0)
white noise type processes, where some WKK results are true only for circular processes,
implying that we can go back in history,
(e.g., undo the US Securities and Exchange Commission,
the Federal Communications Commission,
or go back to horse and buggy, pre $9/11$ days, etc.).
Irreversibility is an important property of most economic time series, making the
assumption of zero memory I(0) process quite unrealistic. Actually, social science
systems are often dynamic, complex and adaptive leading to irreversible, non-stationary
and sometimes rather short time series. Hence Economists often need: (i) `non-standard'
Dickey-Fuller type sampling distributions for testing regression coefficients
(with severe inference problems for panel data), and (ii) detrending and differencing
to convert such series to stationarity. The motivation then is to achieve greater
flexibility and realism by avoiding both (i) and (ii).

\citet{Vinod:04, Vinod:06} offers a computer intensive construction of a plausible ensemble
created from a density satisfying the maximum entropy principle.  The
ME bootstrap algorithm uses
quantiles $x_{j,t}$ for $j=1,\dots,J$ ($J=999$, say), of the ME density as members
of $\Omega$ from the inverse of its `empirical' cumulative distribution function (CDF).
The algorithm guarantees the satisfaction of the ergodic theorem (grand mean of all
$x_{j,t}$ representing the ensemble average equals the time average of $x_t$) and
the central limit theorem.

Some authors try to bring realism by testing and allowing for finite `structural changes',
often with \textit{ad hoc} tools. However, the notion of infinite memory of the random walk
I(1) is unrealistic because the very definitions of economic series (e.g.,
quality and content of the gross domestic product, names of stocks in the
Dow Jones average) change over finite (relatively short) time intervals.
Changing definitions are generally not a problem in natural sciences.
For example, the definition of water or the height of an ocean wave is
unchanged over time.

%%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%

\section{Maximum entropy bootstrap}

%%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%

The bootstrap studies the
relation between the sample and the (unknown) population by a
comparable relation between the sample at hand and
appropriately designed (observable) resamples.  If the observed sample is
independent and identially distributed (iid),
$x_1,. . .  x_T$ are iid random variables with a common original density: $F$.
The joint density of the sample is given by a $T$-fold product: $F^T$. If
$\hat \theta_T$ estimates a parameter $\theta$, the unknown sampling distribution
of ($\hat \theta_T - \theta$) is given by the conditional distribution of
its bootstrap version ($\theta^* - \hat \theta_T$), \citet{Lahiri:03}.
This section describes the ME bootstrap algorithm and indicates how it
extends the traditional iid bootstrap to nonstationary dependent data.

\subsection{The algorithm}

An overview of the steps in Vinod's ME bootstrap algorithm to create a random realization
of $x_t$ is provided in this subsection.
The reader should consult the toy example of the next subsection for concreteness.

\begin{enumerate}
\item Sort the original data in increasing order to create order statistics $x_{(t)}$ and store
the ordering index vector.
\item Compute intermediate points $z_t=(x_{(t)} + x_{(t+1)})/2$ for $t=1, \dots, T-1$
from the order statistics.
\item Compute the trimmed mean $m_\mathrm{trm}$ of deviations
$x_t - x_{t-1}$ among all consecutive observations.
Compute the lower limit for left tail as $z_0=x_{(1)} - m_\mathrm{trm}$
and upper limit for right tail as $z_T= x_{(T)} + m_\mathrm{trm}$. These limits
become the limiting intermediate points.
\item Compute the mean of the maximum entropy density within each interval such that the
`mean-preserving constraint' (designed to eventually satisfy the ergodic theorem)
is satisfied. Interval means are denoted as $m_t$. The means for the
first and the last interval have simpler formulas.
\item Generate random numbers from the $[0,1]$ uniform interval,
compute sample quantiles of the ME density at those points and sort them.
\item Reorder the sorted sample quantiles by using the ordering index of step 1.
This recovers the time dependence relationships of the originally observed data.
\item Repeat steps 2 to 6 several times (e.g., $999$).
\end{enumerate}

\subsection{A toy example}

The procedure described above is illustrated with a small example. Let the sequence
$x_t=(4,12,36,20,8)$ be the series of data observed from the period $t=1$ to $t=5$ as
indicated in the first two columns in Table \ref{Tt5ex}. We jointly sort these two columns
on the second column and place the result in the next two columns
(Table \ref{Tt5ex} columns 3 and 4), giving us the ordering index
vector in column 3.

Next, the four intermediate points in Column 5 are seen to be simple averages of
consecutive order statistics.
We need two more (limiting) `intermediate' points.
These are obtained as described in Step 3 above. Using  10\% trimming, the limiting
intermediate values are $z_0=-11$ and $z_T=51$. With these six $z_t$ values we build
our five half open intervals:
$U(-11,6]\times U(6,10]\times U(10,16]\times U(16,28]\times U(28,51]$.
The maximum entropy density of the ME bootstrap is defined as the combination of $T$
uniform densities defined over (the support of) $T$ half open intervals.

\begin{table}[ht]
\centering
\small{
\begin{tabular}{lllllllll}
\\
\hline
\multirow{3}{0.7cm}{Time} & \multirow{3}{0.5cm}{$x_t$} &
\multirow{3}{1.2cm}{Ordering vector} & \multirow{3}{1cm}{Sorted $x_t$} &
\multirow{3}{1.3cm}{Interme-diate points} & \multirow{3}{1.3cm}{Desired means} &
\multirow{3}{1.3cm}{Uniform draws} & \multirow{3}{1.3cm}{Preli-minary values} &
\multirow{3}{1.3cm}{Final replicate} \\ \\ \\

\hline
\multicolumn{1}{c}{1} & \multicolumn{1}{c}{4} &
\multicolumn{1}{c}{1} & \multicolumn{1}{c}{4} &
\multicolumn{1}{c}{6} & \multicolumn{1}{c}{5} & \multicolumn{1}{c}{0.12} &
\multicolumn{1}{c}{5.85} & \multicolumn{1}{c}{5.85} \\
%
\multicolumn{1}{c}{2} & \multicolumn{1}{c}{12} & \multicolumn{1}{c}{5} &
\multicolumn{1}{c}{8} & \multicolumn{1}{c}{10} & \multicolumn{1}{c}{8} &
\multicolumn{1}{c}{0.83} & \multicolumn{1}{c}{6.70} & \multicolumn{1}{c}{13.90} \\
%
\multicolumn{1}{c}{3} & \multicolumn{1}{c}{36} & \multicolumn{1}{c}{2} &
\multicolumn{1}{c}{12} & \multicolumn{1}{c}{16} & \multicolumn{1}{c}{13} &
\multicolumn{1}{c}{0.53} &
\multicolumn{1}{c}{13.90} &\multicolumn{1}{c}{23.95} \\
\multicolumn{1}{c}{4} & \multicolumn{1}{c}{20} & \multicolumn{1}{c}{4} &
\multicolumn{1}{c}{20} & \multicolumn{1}{c}{28} & \multicolumn{1}{c}{22} &
\multicolumn{1}{c}{0.59} & \multicolumn{1}{c}{15.70} & \multicolumn{1}{c}{15.70} \\
%
\multicolumn{1}{c}{5} & \multicolumn{1}{c}{8} & \multicolumn{1}{c}{3} &
\multicolumn{1}{c}{36} & &
\multicolumn{1}{c}{32} & \multicolumn{1}{c}{0.11} & \multicolumn{1}{c}{23.95} &
\multicolumn{1}{c}{6.70} \\
\hline
\end{tabular}
}
\caption{\label{Tt5ex} Example of the ME bootstrap algorithm.}
\end{table}

The ME density is
shown in Figure~\ref{Ft5ex.density} along with the five
(half-open) intervals. Note that these intervals join all intermediate points $z_t$
(those in column 5 plus two limiting ones) without gaps.

\begin{figure}[ht]
\begin{center}
\includegraphics[width=0.5\textwidth]{t5ex_density}
\end{center}
\caption{\label{Ft5ex.density} Maximum entropy density for the $x_t=4,12,36,20,8$ example.}
\end{figure}

The uniform densities are also designed to satisfy the `mean-preserving constraint',
by making sure that the interval means for the uniform density, $m_t$, satisfy the
following relations:
%
\begin{eqnarray*}
m_1 &=& 0.75 x_{(1)} + 0.25 x_{(2)} \,,\quad \hbox{for the lowest interval,} \\
m_k &=& 0.25 x_{(k-1)} + 0.50 x_{(k)} + 0.25 x_{(k+1)} \,,\quad \hbox{for } k=2,\dots,T-1 \\
m_T &=& 0.25 x_{(T-1)} + 0.75 x_{(T)} \,,
\end{eqnarray*}
%
where $x_{(t)}$ are the order statistics. The desired means using these formulas for the
toy example are reported in column 6.

Finally, random numbers from the $[0,1]$ uniform intervals are independently drawn to compute quantiles
of the ME density. (See left side plot in Figure~\ref{Ft5ex}.) The ME density quantiles
obtained in this way provide a monotonic series. The final replicate is obtained after
recovering the original order sorting column 8 according to the index order given
in column 3. (See right side plot in Figure~\ref{Ft5ex}.)

\begin{figure}[ht]
\begin{center}
\includegraphics[width=\textwidth]{t5ex}
\end{center}
\caption{\label{Ft5ex} Example of the ME bootstrap algorithm.}
\end{figure}

\subsection{Contrast with traditional iid bootstrap} \label{sec:contrast}

\citet{Singh:81} used Edgeworth expansions to confirm the superiority
of iid boot. He also proved that iid-boot fails for dependent data.
See \citet[Chapter~8]{Davison-Hinkley:97} and \citet{Lahiri:03} for more recent
results.
A modification of the iid boot for stationary m-dependent data called
the `block bootstrap' is extensively discussed by
\citet{Lahiri:03}. However, if the evolutionary data are non-stationary,
one cannot always use `differencing' operations to render
them stationary.  The ME bootstrap algorithm is more general, since it does
not assume stationarity and does not need possibly
 `questionable' differencing operations.

In addition to avoiding stationarity, \citet{Vinod:04, Vinod:06}
mentions that it is desirable to avoid the following
three properties of traditional iid bootstrap.

\begin{itemize}
\item The traditional bootstrap sample obtained from shuffling with replacement
repeats some $x_t$ values while not using as many others.
It never admits nearby data values in a resample.
We are considering applications where there is no reason to believe that values
near the observed $x_t$
are impossible.  For example, let $x_t=49.2$. Since $49.19$ or $49.24$, both of which
round to $x_t=49.2$, there is no justification for excluding all such values.
%
\item The traditional bootstrap resamples must lie in the closed interval
$[\hbox{min}(x_t), \hbox{max}(x_t)]$.
Since the observed range is random, we cannot rule out somewhat smaller or larger $x_t$.
Note that the third step of our algorithm implies a less restrictive and wider
range $[z_0, z_T]$.
%
\item The traditional bootstrap resample shuffles $x_t$ such that any dependence
information in the time series sequence $(x_1, \dots, x_t, x_{t+1}, \dots, x_T)$ is lost
in the shuffle.  If we try to restore the original order to the shuffled resample of the
traditional bootstrap, we end up with essentially the original set $x_t$, except that
some dropped $x_t$ values are replaced by the repeats of adjacent values. Hence, it is
impossible to generate a large number J of sensibly distinct resamples with the
traditional bootstrap shuffle without admitting nearby values.
\end{itemize}

\subsection{Shape retention}

The $j$-th ME boot resample  $\{x_{j,t} \}$
retains the shape, or local peaks and troughs, of the original time series
{$x_t$}, by being `strongly dependent' on it. We now imagine that the time series
$x_t$ represents a set (or bundle)
of levels of `utility' enjoyed by someone.  Economic theorists do not like to make
interpersonal comparisons of utility,
since two persons can never really `feel' exactly the same level of satisfaction.
Yet economists must compare utilities
to make policy recommendations by considering preference orderings based on
`ordinal utility theory,' which says that utilities experienced by two individuals
can be made comparable to each other,
provided the two utility bundles satisfy a common partial ordering. Indeed our ME
boot resamples do satisfy a common
partial ordering, since their ranks match perfectly.

Imagine that the original $\{x_t\}$ represents the evolving time path for an
individual's income, sensitive to initial resources at birth and intellectual
endowments with a corresponding path of utility (enjoyment) levels. Our ME boot
algorithm creates reincarnations of these paths ensuring that ordinal utilities are
comparable across reincarnations,
retaining just enough of the basic shape of $x_t$. See \citet{Henderson-Quandt:80} for
a discussion of multi-period consumption and ordinal utility.


Next we provide an example of how ME boot retains the shape as well as the periodicity
of the original series by using the \code{AirPassengers}
time series available in \proglang{R}.

\subsubsection[Example: AirPassengers time series]{Example: \code{AirPassengers} time series}

\begin{figure}[t!]
\begin{center}
\includegraphics[width=\textwidth]{airp1}
\end{center}
\caption{\label{Fairp} Replicate for the \code{AirPassengers} time series.}
\end{figure}

Figure~\ref{Fairp} displays the \code{AirPassengers} time series
along with a replicate of the series. An animation showing different replicates
is available as a supplemental AVI file along with \cite{Vinod+Lopez-de-Lacalle:2009}.
The autocorrelation function and the log-periodogram are shown for each replicate.
One can see that, retaining the
shape of the original series, the replicates remain close to the time and frequency domain
properties of the series, without imposing any parametric restrictions.



%%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%

\section{Applications}

%%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%

\subsection{Consumption function}

This example describes how to carry out inference through the ME boot ensemble
in the following regression:

\begin{equation}
\label{Ecnsmptn}
c_t = \beta_1 + \beta_2\,c_{t-1} + \beta_3\,y_{t-1} + u_t,
\end{equation}
%
for the null hypothesis $\beta_3=0$.

We use the annual data set employed in \citet[pp.~799--801]{Murray:06} to discuss
a Keynesian consumption function on the basis of the Friedman's permanent income
hypothesis (PIH) and a simpler version of Robert Hall's model.
The data are the logarithm of the US consumption, $c_t$, and disposable income, $y_t$,
in the period 1948--1998. The packages \pkg{car} \citep{Fox:2002}
and \pkg{lmtest} \citep{Zeileis+Hothorn:2002}
will be useful to extract information from linear regression models. We use
the interface in package \pkg{dynlm} \citep{dynlm} for dynamic linear regression.
%
<<>>=
library("meboot")
library("car")
library("lmtest")
library("dynlm")
data("USconsum")
USconsum <- log(USconsum)
lmcf <- dynlm(consum ~ L(consum, 1) + L(dispinc, 1), data = USconsum)
coeftest(lmcf)
set.seed(135)
durbinWatsonTest(model = lmcf, max.lag = 4)
@
%
The residuals are serially uncorrelated since the $p$~values of the
generalized Durbin-Watson (DW) statistics up to order 4 are larger than the significance level 0.05. The seed was needed
in the above code
for a reproducible computation of $p$~values for the DW statistics.
The estimated coefficient of lagged income, $\hat \beta_3=0.027$,
with the standard error $se=0.1439,$ is statistically
insignificant. The 95\% confidence interval  $(-0.263, 0.316)$ has the zero inside
this interval.

This result was initially interpreted as supporting Friedman's PIH.
However, the large unit root literature argued that the
sampling distribution of $\hat \beta_3$ is nonstandard, and that traditional
inference based on the Student's $t$ or asymptotic
normal distributions may lead to spurious results.
Hence, these days, one uses unit root tests to decide whether
differencing or de-trending of $c_t$ and $y_t$
would make all variables in a regression integrated of the same order, say $I(0)$.
The critical values from a Dickey-Fuller type nonstandard
density (originally obtained by a simulation) replace the usual Student's $t$
critical values. Our bootstrap also reveals any nonstandard features
of the sampling distribution and confidence intervals
specific to the problem at hand, avoiding the use of
critical values altogether. Thus we can cover a wide variety
of situations beyond the one simulated
by Dickey and Fuller.


Instead of resampling the residuals, our ME bootstrap
resamples all time series in the regression themselves
by following the `resampling cases' bootstrap method.
Three advantages of this method
noted by
\citet[Section~6.2.4]{Davison-Hinkley:97}
are:
(a) This method does  not use any  simulated errors
based on the assumed reliability of a parametric model.
(b) It does not need
to assume that the
conditional mean of the dependent variable given a realization of regressors
($E(y |X=x)$ in standard notation) is linear.
(c)  It is robust against heteroscedastic errors.


Now we briefly describe the `resampling cases' method
in the context of time series regressions, where
the `case' refers to time.
From (\ref{Ecnsmptn}) it is intuitively clear that
we should resample
only the two `original' time series
$c_t$ and $y_t,$ and then lag them as needed instead
of blindly resampling
$(c_t, c_{t-1}, y_{t-1})$  all three variables in the model.  Our bootstrap
inference will rely on
a confidence interval for any function $\theta=f(\beta)$ of coefficients $\beta$.
For example, $\theta=\beta_3$ for assessing the Friedman hypothesis
based on (\ref{Ecnsmptn}).

<<>>=
theta <- function(y, x) {
  reg <- dynlm(y ~ L(y, 1) + L(x, 1))
  thet <- coef(reg)[3]
  return(thet)
}
@

The above code represents our choice of
simplicity over generality.
It is intended to be used upon
replacing the \code{y} by
$c_t$ and the \code{x} by $y_t,$
for its use as $\theta=\beta_3$ in (\ref{Ecnsmptn}).
For any other example it
provides a template, needing modifications.
If a researcher wishes to analyze
the scale elasticity of a Cobb-Douglas type
production function, \citet[pp.~10--11]{Vinod:08},
the regression becomes $y = \beta_1 x_1 +\beta_2 x_2$.
Then the parameter of interest:
$\theta=\beta_1+\beta_2,$ is a sum
of two slope coefficients.  The modified
\code{theta} for this example denoted by
\code{theta.cobbd} is given by
the following code:
%
<<>>=
theta.cobbd <- function(y, x1, x2) {
  reg <- lm(y ~ x1 + x2)
  thet <- coef(reg)[2] + coef(reg)[3]
  return(thet)
}
@

In general, a modification of \code{theta}
can involve a nonlinear function
of several coefficients. For example, \citet[Section~3.2]{Vinod:08},
if the parameter of interest is the
long-run multiplier, it becomes a nonlinear function.
The main point is
that our $\theta$ refers to only one parameter of interest.
Any researcher interested in  two or more parameters
can readily
repeat our procedure as often as needed.

The following function
called \code{bstar.consu} generates
a large number of bootstrap single parameter estimates. The \code{bstar} in its name
suggests that resamples of the third regression coefficient might be denoted
as $\{b_3^*\}$.  More important,
it is a template, expecting modifications.
Its initial arguments refer to data on all `original' time series
(not counting leads and lags as separate series) using the notation
$y$ for the dependent variable and $x$ for the regressor.  It is flexible,
allowing the user to choose the
confidence level (default: 95\%), the \proglang{R} function
\code{theta} (defining the parameter of interest must be predefined),
size of resamples as \code{bigJ} (default: \code{bigJ = 999})
and the seed
for random number generator as \code{seed1} (default: \code{seed1 = 135}).
%
<<>>=
bstar.consu <- function(y, x, theta,
  level = 0.95, bigJ = 999, seed1 = 135) {
  set.seed(seed1)
  semy <- meboot(x = y, reps = bigJ)$ensemble
  semx <- meboot(x = x, reps = bigJ)$ensemble
  n <- NROW(y)
  m <- length(theta(y, x))
  if(m!=1) stop("too many parameters in theta")
  bb <- matrix(NA, bigJ)
  for(j in 1:bigJ) {
    yy <- semy[,j]
    xx <- semx[,j]
    bb[j] <- theta(yy, xx)
  }
  return(bb)
}
@

Since the Cobb-Douglas
model involves regressing $y$ on $x_1$ and $x_2$, its
function (called\linebreak \code{bstar.cobbd} below) has an additional input \code{x2}. It
calls the function \code{meboot}
thrice for \code{y, x1} and \code{x2}. Also,
the input to the function \code{theta.cobbd} needs both \code{xx1}
and \code{xx2} instead of simply \code{xx}, in the code \code{bstar.consu}
above. We believe
that it is easy to make such changes to our
simple and intuitive \code{bstar} type functions.
The template \code{bstar.cobbd},
for the two regressor Cobb-Douglas case below,
explicitly shows how to extend the function
\code{bstar.consu} to
two or more regressors by using \code{x2}, \code{x3}, \code{x4}, \dots as needed.

<<>>=
bstar.cobbd <- function(y, x1, x2, theta = theta.cobbd,
  level = 0.95, bigJ = 999, seed1 = 135) {
  set.seed(seed1)
  semy <- meboot(x = y, reps = bigJ)$ensemble
  semx1 <- meboot(x = x1, reps = bigJ)$ensemble
  semx2 <- meboot(x = x2, reps = bigJ)$ensemble
  n <- NROW(y)
  m <- length(theta.cobbd(y, x1, x2))
  if(m!=1) stop("too many parameters in theta")
  bb <- matrix(NA, bigJ)
  for(j in 1:bigJ) {
    yy <- semy[,j]
    xx1 <- semx1[,j]
    xx2 <- semx2[,j]
    bb[j] <- theta.cobbd(yy, xx1, xx2)
  }
  return(bb)
}
@

Now we return to
constructing an approximation to the
sampling distribution of $\hat \beta_3$
in (\ref{Ecnsmptn}),
without having to assume that the distribution is
Student's $t$ or Dickey-Fuller.
That is, we use the output of the function \code{bstar.consu}
to construct a confidence interval for $\beta_3$ to
help decide whether  $\hat \beta_3$ is statistically significantly different from zero.
Assuming the earlier code is in the memory, let us begin
by computing the simplest percentile interval, using
the function \code{quantile} of \proglang{R}, while choosing \code{type = 8}
\citep[as recommended by][see also \code{help("quantile")}]{Hyndman+Fan:1996}.
%
<<>>=
y <- USconsum[,2]
x <- USconsum[,1]
reg <- dynlm(y ~ L(y, 1) + L(x, 1))
su <- summary(reg)
se <- su$coefficients[3,2]
t0 <- theta(y, x)
b3s <- bstar.consu(y, x, theta)
simple.percentile <- quantile(b3s, c(0.025, 0.975), type = 8)
asymmetric.around.0 <- null.ci(b3s)
out <- list(t = b3s, t0 = t0, var.t0 = se^2, R = 999)
class(out) <- "boot"
library("boot")
boot.percentile <- boot.ci(out, type = "perc")$percent[4:5]
boot.norm <- boot.ci(out, type = "norm")$normal[2:3]
boot.basic <- boot.ci(out, type = "basic")$basic[4:5]
rbind(simple.percentile, asymmetric.around.0, boot.percentile,
  boot.norm, boot.basic)
@
%
The code above reports four intervals beyond the
simplest one mentioned before. The
\pkg{meboot} package includes
the function \code{null.ci} (an elegant improvement by Achim Zeileis of our
function \code{zero.ci}) which provides asymmetric confidence intervals
arond a specified null value (=0, here).  The names of
three confidence intervals have the prefix boot to remind
us that they come from
the \pkg{boot} package \citep{Canty+Ripley:2009}.  These are
available only after \code{out} is defined
with a suitable \code{list} and \code{boot.ci} function
is called with appropriate options.
These options
provide
some of the well known refinements to the
percentile confidence interval from the bootstrap literature.


Statistical theory behind these refinements is
mentioned at the beginning of Section 2.
In the present context,
bootstrap estimates
of $\theta$ (see \code{b3s} above) are
$\hat \theta_j^*$, with
$j=1,2,\dots, J$. If the standard error $se$ of
$\hat \theta$ is known, then
$(\hat \theta_j^* - \hat \theta) /se$ values provide
a good approximation to the sampling distribution of
$(\hat \theta -  \theta)/se$, the Wald statistic.
The code in \code{boot.ci} is designed to
correct for bias and improve asymptotic
properties of bootstrap confidence intervals.

Finally, let us consider sophisticated confidence
intervals based on highest density regions (HDR) of sampling distributions,
\citet{Hyndman:96}.  If $f(\hat\theta)$ is the
density, and $\alpha$ is the type~I error
($=0.05$, say),
then the $100(1-\alpha)\%$
HDR is the subset of the
sample space of the random variable such that
\begin{equation}
\label{EHDR}
\mathit{HDR}(f_{\alpha}) = \{\hat\theta :  f(\hat\theta) \ge f_{\alpha} \},
\end{equation}
where $f_{\alpha}$ is the largest constant such that the following
probability statement holds true:
$Pr(\hat\theta \in \mathit{HDR}(f_{\alpha}) ) \ge 1-\alpha$.
Highest density means every point inside the HDR has
probability density at least as large as every point outside the HDR.
When the sampling distribution is bimodal or multimodal, HDR seems to be
a reliable way of finding confidence regions.
\citet{Hyndman:96} discusses many advantages of HDR methods.
We use the \proglang{R} package \pkg{hdrcde} \citep{hdrcde}
to find HDR regions with graphics for a study of the
sampling distribution of $\hat\theta$ under the null.
It also reports the value of $f_{\alpha}$ appearing
in equation (\ref{EHDR}).

<<hdrcde, fig=TRUE, include=FALSE, height=5, width=5>>=
library("hdrcde")
hdr.den(b3s, main = expression(Highest ~ density ~ region ~
  of ~ beta [3] ~ estimates :  ~  Hall ~ model))
@

Note that even the 50\% confidence region (HDR) starts at nearly zero, while
95\% region decidedly covers the zero.  However
the largest constants $f_{\alpha}$ are all positive.
Our \pkg{meboot}
results (including the HDR) support Friedman's PIH,
since zero is inside all 95\% confidence intervals for $\beta_3$.


\begin{figure}[t!]
\begin{center}
\includegraphics[width=0.7\textwidth]{meboot-hdrcde}
\end{center}
\caption{\label{HDregion} Highest density region for the sampling
distribution of $\hat\beta_3$ using Hall's model.}
\end{figure}





\subsection{Assessment of the Fed effect on stock prices using panel data}

This example shows how the ME bootstrap can be employed for panel data analysis.
Our example is from \citet{Vinod:02}
where the effect of monetary policy (interest rates) on prices and their `turning points'
in the stock market is evaluated in greater detail.

The `Fed effect' discussed in the financial press refers to a rally in the S\&P 500
index of stock prices a few days before the Federal Reserve Bank (Fed)
policy makers' meeting and a price decline after the meeting.
This example focuses on the longer term than daily price fluctuations by using the monthly
data (May 1993 to November 1998 with $T=67$) for stocks with ticker symbols: ABT, AEG, ATI,
ALD, ALL, AOL, and AXP and regard this as a representative sample of
the market containing $N=7$ individual companies.

Note that when the Fed adjusts the Fed funds rate, it affects market
expectations and, hence, the interest on 3-month Treasury bills ($\mathit{Tb3}$),
the key short-run interest
rate in the economy. Our simple model of monetary policy regresses the stock price ($P$)
on the natural log of market capitalization ($\mathit{LMV}$, as a control
variable for the size of the firm) and the $\mathit{Tb3}$. We write:
%
\begin{equation} \label{Efed}
P_{it} = \beta_1 + \beta_2\, \mathit{LMV}_{it} +
\beta_3\, \mathit{Tb3}_{it} + \varepsilon_{it}\,,
\end{equation}
%
where the subscript $it$ refers to $i$-th individual (company) at time $t$ and where
$\varepsilon_{it}$ are assumed to be iid. The Fed effect is present, if the
coefficient of the variable $\mathit{Tb3}$
in equation (\ref{Efed}) is statistically significant.

We use the \proglang{R} package \pkg{plm}  \citep{Croissant+Millo:2008}
for basic estimation of panel data models
before any bootstrap. It expects that the data
be in the form of a \code{data.frame} object.
Accordingly, the package \pkg{meboot} provides the data
for this example as a \code{data.frame} object called \code{ullwan}.
Let us replace the third column containing the `market value of the firm'
by its logarithms denoted by LMV within the data frame object.
Since the data setup is critical, it is perhaps useful to
illustrate our (slightly revised) data frame \code{ullwan} by displaying the
initial and ending observations.
Note that the first column
entitled \code{Subj} contains the
identification numbers 1 to 7, for the 7 ticker symbols included in the data set.
Note that all time series for the first ticker symbol
ABT are placed together at the beginning of the data set.
These are viewed by using the command:
\code{head(ullwan)}.  Trailing data for observation numbers
464 to 469 for the last ticker symbol AXP are  viewed by using the command:
\code{tail(ullwan)}
in the following code.
%
<<>>=
library("plm")
data("ullwan")
attach(ullwan)
LMV <- log(MktVal)
ullwan[,3] <- LMV
names(ullwan)[3] <- "LMV"
head(ullwan)
tail(ullwan)
@

\subsubsection{Pooled effects}

Pooled regression means combining the cross-sectional data and time series data into
one large set of $T\times N (=67\times 7=469$ here) observations.
We note below that Student's $t$~value and the corresponding $p$~value from the pooled
model suggest a highly significant $\mathit{Tb3}$
regressor implying that the Fed announcement does
have a statistically significant effect on the level of stock prices.
The multiple $R^2$ is $0.497$,
which becomes $0.495$
when adjusted for degrees of freedom.

<<>>=
summary(lm(Price ~ LMV + Tb3))
@

The
$t$~value for the coefficient of $\mathit{Tb3}$
from the pooled model suggests that the Fed does
have a statistically significant effect on the level of stock prices in a pooled
regression.


The default use of the function \code{meboot} is illustrated in
the earlier subsection
in \code{bstar.consu} and \code{bstar.cobbd}, where the argument
\code{x} represents a single vector of numbers (usually time series).  In this subsection we require \code{meboot}
to create $J$
replicates over time, separately for all $N$ individuals to suit the panel data.
Since the \pkg{plm} package expects
the panel data in the form of a \code{data.frame}
object, our
function \code{meboot} is designed to expect similarly organized data.
For example, since the identifier for individual (company ticker) called
\code{Subj} is
located in the first column of the \code{ullwan} data frame, the \code{meboot}
would expect \code{colsubj=1} as the argument.
It is necessary to call the \code{meboot} function separately for
each relevant column of the data frame
identified by its column number denoted as the argument \code{coldata}.
For example, we set \code{coldata = 3} for LMV since third
column has data on LMV.
Mere presence of non-null values for
\code{colsubj} and \code{coldata} in the call to \code{meboot}
triggers it
to implement its panel data version.


The following code creates
$J=999$ ensembles for the $T=67$ time series points for the $N=7$ stocks separately
for the three variables in our regression ($P$, $\mathit{LMV}$ and $\mathit{Tb3}$).
Each call yields an ensemble of $1000$ sets of $67\times 7=469$ data points, upon including
the original data as the first column and $999$ additional columns of similarly evolving
time series.
%
<<>>=
jboot <- 999
set.seed(567)
LMV.ens <- meboot(x = ullwan, reps = jboot, colsubj = 1, coldata = 3)
Price.ens <- meboot(x = ullwan, reps = jboot, colsubj = 1, coldata = 4)
Tb3.ens <- meboot(x = ullwan, reps = jboot, colsubj = 1, coldata = 6)
@

The purpose of the ME bootstrap here is to assess whether the effect of the Fed announcement
continues to be significant for the pooled and other models described below. The slope
coefficients based on the ME bootstrap ensembles (created above) can be computed as follows.

<<>>=
slopeTb3 <- slopeLMV <- rep(0, jboot)
for(j in 1:jboot) {
  frm <- data.frame(Subj = ullwan[,1], Time = ullwan[,2],
    Price = Price.ens[,j], Tb3 = Tb3.ens[,j], LMV = LMV.ens[,j])
  frm <- pdata.frame(frm, 7)
  gip <- coef(plm(Price ~ LMV + Tb3, model = "pooling", data = frm))
  slopeTb3[j] <- gip[3]
  slopeLMV[j] <- gip[2]
}
Percentile.Tb3 <- quantile(slopeTb3, c(0.025, 0.975), type = 8)
Refined.Tb3 <- null.ci(slopeTb3)
Percentile.LMV <- quantile(slopeLMV, c(0.025, 0.975), type = 8)
Refined.LMV <- null.ci(slopeLMV)
rbind(Percentile.Tb3, Refined.Tb3, Percentile.LMV, Refined.LMV)
@

The 95\% ME bootstrap confidence
intervals for the control variable $\mathit{LMV}$ has all
estimated slopes positive and the lower limit of
of the asymmetric (refined) 95\% interval
is simply the smallest slope.   It suggests that
$\beta_2$ in (\ref{Efed}) for the pooled model is statistically significantly positive.
That is, it is worthwhile to include the control variable in the model.

The 95\% ME bootstrap percentile confidence interval for the slope coefficient of $\mathit{Tb3}$
is shown above.  Since all estimated slopes by the ME bootstrap are negative
with the null value zero, the upper limit of the asymmetric (refined) 95\% interval
is simply the largest slope.
It is interesting to
also report additional advanced confidence intervals from
the package \pkg{boot} by using the function \code{boot.ci}
with some preliminary code to conform with its protocol.

<<>>=
thetp <- plm(Price ~ LMV + Tb3, model = "pooling", data = ullwan)
varTb3 <- thetp$vcov[3,3]
plm1 <- coef(thetp)
t0Tb3 <- plm1[3]
t0LMV <- plm1[2]
out2 <- list(t = as.matrix(slopeTb3), t0 = t0Tb3,
  var.t0 = varTb3, R = 999)
class(out2) <- "boot"
boot.percentile <- boot.ci(out2, type = "perc")$percent[4:5]
boot.norm <- boot.ci(out2, type = "norm")$normal[2:3]
boot.basic <- boot.ci(out2, type = "basic")$basic[4:5]
rbind(Percentile.Tb3, boot.percentile, boot.norm, boot.basic)
@

These results clearly suggests that for the pooled model
$\beta_3$ in (\ref{Efed}) is statistically significantly negative.
%
<<>>=
znp <- pvcm(Price ~ LMV + Tb3, data = ullwan, model = "within")
zplm <- plm(Price ~ LMV + Tb3, data = ullwan)
pooltest(zplm, znp)
@
%
The function \code{pvcm} refers to panel variable coefficients models.
If pooling is appropriate the coefficients for individual units do
not significantly differ from one another.
The high value of the $F$~statistic in the output of
the \code{pooltest}  suggests that pooling
may not be appropriate. Hence we need to consider a `random effects' model next.

\subsubsection{Random effects}

The random effects model results are obtained by setting
\code{model = "random"} in the arguments of the
function \code{plm}.

<<>>=
gir <- plm(Price ~ LMV + Tb3, data = ullwan, model = "random")
coef(gir)
@

Using the ensembles created above for all data variables in equation (\ref{Efed})
we implement the ME bootstrap for the random effects model and print various confidence
intervals as follows.

<<>>=
slopeTb3 <- slopeLMV <- rep(0, jboot)
for(j in 1:jboot) {
  frm <- data.frame(Subj = ullwan[,1], Tim = ullwan[,2],
    Price = Price.ens[,j], Tb3 = Tb3.ens[,j], LMV = LMV.ens[,j])
  frm <- pdata.frame(frm, 7)
  gip <- coef(plm(Price ~ LMV + Tb3, model = "random", data = frm))
  slopeTb3[j] <- gip[3]
  slopeLMV[j] <- gip[2]
}
Percentile.Tb3 <- quantile(slopeTb3, c(0.025, 0.975), type = 8)
Refined.Tb3 <- null.ci(slopeTb3)
Percentile.LMV <- quantile(slopeLMV, c(0.025, 0.975), type = 8)
Refined.LMV <- null.ci(slopeLMV)
rbind(Percentile.Tb3, Refined.Tb3, Percentile.LMV, Refined.LMV)
@

Now we
use the function \code{boot.ci}
after some preliminary code to conform with its protocol
to obtain additional `random effects' confidence intervals for the coefficient
of $Tb3$.

<<>>=
thetr <- plm(Price ~ LMV + Tb3, model = "random", data = ullwan)
varTb3 <- thetr$vcov[3,3]
plm1 <- coef(thetr)
t0Tb3 <- plm1[3]
out3 <- list(t = as.matrix(slopeTb3), t0 = t0Tb3, var.t0 = varTb3, R = 999)
class(out3) <- "boot"
boot.percentile <- boot.ci(out3, type = "perc")$percent[4:5]
boot.norm <- boot.ci(out3, type = "norm")$normal[2:3]
boot.basic <- boot.ci(out3, type = "basic")$basic[4:5]
rbind(boot.percentile, boot.norm, boot.basic)
@

The random effects 95\% ME bootstrap confidence intervals using the 999 replicates of
data are essentially similar to the pooled data results, allowing
us to conclude that both $\beta_2$ and $\beta_3$ in (\ref{Efed}) are significantly
different from zero.  Since the 95\% confidence intervals for $\beta_3$ do not cover
the zero, we can conclude that the `Fed effect' is significant for the random effects
panel data model.

%%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%

\section{Concluding remarks}

%%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%

This paper illustrates the performance and usage of Vinod's maximum entropy bootstrap
for dependent data by using econometric examples, including one involving panel
(longitudinal) data. Besides econometrics, at least some time series in biology,
engineering and social sciences are similarly
state-dependent and subject to structural changes and discontinuities.
All such series cannot be transformed into stationary series without impairing
our understanding of underlying relations among them.
The \pkg{meboot} \proglang{R} package not only fills a gap in the bootstrap toolkit,
but is particularly useful as
it permits simpler model specifications (allowing a direct use of one or more such
time series without first making them stationary) and convenient statistical inference
(avoiding non-standard Dickey-Fuller type sampling distributions).

%%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%    %%%%%%%%

\section*{Acknowledgments}

We thank the referee, Achim Zeileis and Johanna Francis for helpful comments.

\bibliography{meboot}

\end{document}
