[Eventstudiescommits] r132  pkg/vignettes
noreply at rforge.rproject.org
noreply at rforge.rproject.org
Sat Aug 17 12:36:28 CEST 2013
Author: vimsaa
Date: 20130817 12:36:28 +0200 (Sat, 17 Aug 2013)
New Revision: 132
Modified:
pkg/vignettes/eventstudies.Rnw
Log:
Edits for the first few sections of the vignette.
Modified: pkg/vignettes/eventstudies.Rnw
===================================================================
 pkg/vignettes/eventstudies.Rnw 20130815 12:01:35 UTC (rev 131)
+++ pkg/vignettes/eventstudies.Rnw 20130817 10:36:28 UTC (rev 132)
@@ 8,301 +8,292 @@
\usepackage{parskip}
\usepackage{amsmath}
\title{Introduction to the \textbf{eventstudies} package in R}
\author{Ajay Shah, Vimal Balasubramaniam, Vikram Bahure and Renuka
 Sane}
+\author{Vikram Bahure and Renuka Sane and Ajay Shah\thanks{We thank
+ Chirag Anand for valuable inputs in the creation of this vignette.}}
\begin{document}
%\VignetteIndexEntry{eventstudies: A package with functionality to do Event Studies}
%\VignetteDepends{}
%\VignetteKeywords{event studies}
%\VignettePackage{eventstudies}
+% \VignetteIndexEntry{eventstudies: A package with functionality
+% to do Event Studies} \VignetteDepends{} \VignetteKeywords{event
+% studies} \VignettePackage{eventstudies}
\maketitle
+
\begin{abstract}
 Event study analysis is a ubiquitous tool in the study of the impact
 of events on the value of a firm. There is, however, no single
 repository to undertake such an analysis with
 R. \textbf{eventstudies} provides the toolbox to carry out an
 eventstudy analysis. It contains functions to calculate measures of
 firm returns, convert a dataset to event time and procedures for
 inference.
+ Event study analysis is a ubiquitous tool in the econometric
+ analysis of an event and its impact on the measured
+ outcome. Although widely used in finance, it is a generic tool
+ that can be used for other purposes as well. There is, however,
+ no single repository to undertake such an analysis with
+ R. \texttt{eventstudies} provides the toolbox to carry out an
+ eventstudy analysis. It contains functions to transform data
+ into the eventtime frame and procedures for statistical
+ inference. In this vignette, we provide a finance example and
+ utilise the rich features of this package.
\end{abstract}
\SweaveOpts{engine=R,pdf=TRUE}
\section{Introduction}
Event study methodology has been primarily used to evaluate the impact
of specific events on the value of the firm. The typical procedure for
conducting an event study involves \citep{MacKinlay1997}:
\begin{itemize}
\item Defining the event of interest and the event window. The event
 window should be larger than the specific period of
 interest. % Generally the event
 % period itself is not included in the estimation period to prevent
 % the event from influencing the normal performance model parameter
 % estimates.
 \item Determining a measure of abnormal returns, the most common
 being the \textit{constant mean return model} and the
 \textit{market model}. This is important to disentangle the effects
 on stock prices of information that is specific to the firm under
 question (e.g. stock split announcement) and information that is
 likely to affect stock prices marketwide (e.g. interest rates).
 \item Analysis of firm returns around the event date.
\end{itemize}
+Event study methodology has been primarily used to evaluate the
+impact of specific events on the value of a firm. The typical
+procedure for conducting an event study involves
+\citep{MacKinlay1997}:
+\begin{enumerate}
+\item Defining the event of interest and the event window. The
+ event window should be larger than the specific period of
+ interest.
+\item Determining a measure of abnormal returns, the most common
+ being the \textit{constant mean return model} and the
+ \textit{market model}. This is important to disentangle the
+ effects on stock prices of information that is specific to the
+ firm under question (e.g. stock split announcement) and
+ information that is likely to affect all stock prices
+ (e.g. interest rates).
+\item Analysis of firm returns on or after the event date.
+\end{enumerate}
The \textbf{eventstudies} package brings together the various aspects
of an event study analysis in one library. It provides for functions
to calculate returns, transform data into eventtime, and inference
procedures. All functions in this package are implemented in the R
system for statistical computing. The package, and R are available at
no cost under the terms of the general public license (GPL) from the
comprehensive R archive network (CRAN,
\texttt{http://CRAN.Rproject.org}).
+The \textbf{eventstudies} package brings together the various
+aspects of an event study analysis in one library. It provides for
+functions to calculate returns, transform data into eventtime,
+and inference procedures. All functions in this package are
+implemented in the R system for statistical computing. The
+package, and R are available at no cost under the terms of the
+general public license (GPL) from the comprehensive R archive
+network (CRAN, \texttt{http://CRAN.Rproject.org}).
This paper is organised as follows. A skeletal event study model is
presented in Section \ref{s:model}. Section \ref{s:approach} discusses
the software approach used in this package. Section \ref{s:example}
shows an example.
+This paper is organised as follows. A skeletal event study model
+is presented in Section \ref{s:model}. Section \ref{s:approach}
+discusses the software approach used in this package. Section
+\ref{s:example} shows an example.
\section{Skeletal event study model} \label{s:model}
In this section, we present a model to evaluate the impact of stock
splits on returns \citep{Corrado2011}.
+In this section, we present a model to evaluate the impact of
+stock splits on returns \citep{Corrado2011}.
Let day0 identify the stock split date under scrutiny and let days
t = ... 3,2,1 represent trading days leading up to the event. If
the return on the firm with the stock split $R_o$ is statistically
large compared to returns on previous dates, we may conclude that the
stock split event had a significant price impact.
+Let day $0$ identify the stock split date under scrutiny and let
+days t = $...,3,2,1$ represent trading days leading up to the
+event. If the return on the firm with the stock split $R_o$ is
+statistically large compared to returns on previous dates, we may
+conclude that the stock split event had a significant price
+impact.
To disentangle the impact of the stock split on the returns of the
firm from general marketwide information, we use the marketmodel to
adjust the eventdate return, thus removing the influence of market
information.
+firm from general marketwide information, we use the marketmodel
+to adjust the eventdate return, thus removing the influence of
+market information.
The market model is calculated as follows:
\[ R_t = a + b RM_t + e_t \]
The firmspecific return $e_t$ is unrelated to the overall market and
has an expected value of zero. Hence, the expected event date return
conditional on the event date market return is
+The firmspecific return $e_t$ is unrelated to the overall market
+and has an expected value of zero. Hence, the expected event date
+return conditional on the event date market return is
\[ E(R_0RM_0) = a + b RM_0 \]
The abnormal return $A_0$ is simply the dayzero firmspecific return
$e_0$:
+The abnormal return $A_0$ is simply the dayzero firmspecific
+return $e_0$:
\[ A_0 = R_0 E(R_0RM_0) = R_0  a  b RM_0 \]
A series of abnormal returns from previous periods are also calculated
for comparison, and to determine statistical significance.
+A series of abnormal returns from previous periods are also
+calculated for comparison, and to determine statistical
+significance.
\[ A_t = R_t E(R_tRM_t) = R_t  a  b RM_t \]
+\[ A_t = R_t E(R_tRM_t) = R_t  a  b RM_t \]
The event date abnormal return $A_0$ is then assessed for statistical
significance relative to the distribution of abnormal returns $A_t$ in
the control period. A common assumption used to formulate tests of
statistical significance is that abnormal returns are normally
distributed.
+The event date abnormal return $A_0$ is then assessed for
+statistical significance relative to the distribution of abnormal
+returns $A_t$ in the control period. A common assumption used to
+formulate tests of statistical significance is that abnormal
+returns are normally distributed.
\section{Software approach} \label{s:approach}
\textbf{eventstudies} offers the following functionalities:
+
\begin{itemize}
 \item Models for calculating returns
 \item Procedures for converting data to eventtime and remapping
 eventframe
 \item Procedures for inference
+\item Models for calculating returns
+\item Procedures for converting data to eventtime and remapping
+ eventframe
+\item Procedures for inference
\end{itemize}
\subsection{Models for calculating returns}
+
Firm returns can be calculated using the following functions:
+
\begin{itemize}
\item \texttt{excessReturn}: estimation of excess returns i.e. $R_j 
 R_m$ where $R_j$ is the return of firm $j$ and $R_m$ is the market
 return.
\item \texttt{marketResidual}: estimation of market residual after
 extracting market returns from firm returns.
+\item \texttt{excessReturn}: estimation of excess returns
+ i.e. $R_j  R_m$ where $R_j$ is the return of firm $j$ and $R_m$
+ is the market return.
+\item \texttt{marketResidual}: estimation of market model to
+ obtain idiosyncratic firm returns, controlling for the market
+ returns.
+
\item \texttt{AMM}: estimation of the Augmented market model which
 gives the market residual after extracting market returns and
 currency returns from firm returns. The function allows for
 specifying the type of the AMM model as well.
+ provides user the capability to run a multivariate market model
+ with orthogonalisation and obtain idiosyncratic returns.
+
\end{itemize}

The two common arguments for these functions are
\texttt{firm.returns} which is a timeseries of stock returns, and
\texttt{market.returns}, which is a timeseries of market
returns. The type of AMM model is specified with the option
\texttt{amm.type}.
+
+% Once AMM() is rewritten, one paragraph on the onefirmAMM
+% arguments here used with AMM(...).
The output from these models is also a timeseries object. This
becomes the input for converting to event time.
+becomes the input for converting to event time. % Check if I can
+ % work with 'xts' and/or 'zoo'?
\subsection{Converting the dataset to an event time}
The conversion of the returns data to eventtime, and to cumulative
returns is done using the following functions:
+
+The conversion of the returns data to eventtime, and to
+cumulate returns is done using the following functions:
+
\begin{itemize}
\item \texttt{phys2eventtime}: conversion to an event frame. This
 requires a time series object of stock price returns and an object
 with two columns \textit{unit} and \textit{when}, the firms and the
 date on which the event occurred respectively.
+ requires a time series object of stock price returns and an
+ object with two columns \textit{unit} and \textit{when}, the
+ firms and the date on which the event occurred respectively.
 \item \texttt{remap.cumsum}: conversion of returns to cumulative
 returns. The input for this function is the timeseries data in
 eventtime that is the output from \texttt{phys2eventtime}.
+\item \texttt{remap.cumsum}: conversion of returns to cumulative
+ returns. The input for this function is the timeseries data in
+ eventtime that is the output from \texttt{phys2eventtime}.
\end{itemize}
\subsection{Procedures for inference}
Procedures for inference include:
\begin{itemize}
 \item \texttt{inference.bootstrap}: estimation of bootstrap to
 generate the distribution of cumulative returns series.
 \item \texttt{inference.wilcox}: estimation of wilcox inference to
 generate the distribution of cumulative returns series.
 \end{itemize}
+\item \texttt{inference.bootstrap}: estimation of bootstrap to
+ generate the distribution of cumulative returns series.
+
+\item \texttt{inference.wilcox}: estimation of wilcox inference to
+ generate the distribution of cumulative returns series.
+\end{itemize}
 The arguments for both these include \texttt{es.w}, the cumulative
 returns in eventtime. The argument \texttt{to.plot} plots the
 confidence interval around returns series.
+The arguments for both these include \texttt{es.w}, the cumulative
+returns in eventtime. The argument \texttt{to.plot} plots the
+confidence interval around returns series.
\section{Example: Performing eventstudy analysis}
+\section{Example: Performing eventstudy analysis}
\label{s:example}
We demonstrate the package with a study of the impact of stock splits
on the stock prices of firms. We use the returns series of the
thirty index companies, as of 2013, of the Bombay Stock Exchange
(BSE), from 2001 to 2013. We have stock split dates for each firm
from 2000 onwards.
+We demonstrate the package with a study of the impact of stock
+splits on the stock prices of firms. We use the returns series of
+the thirty index companies, as of 2013, of the Bombay Stock
+Exchange (BSE), from 2001 to 2013. We have stock split dates for
+each firm from 2000 onwards.
Our data consists of a \textit{zoo} object for stock price returns for
the thirty firms. This is called \textit{StockPriceReturns} and
another zoo object, \textit{nifty.index}, of market returns.
+Our data consists of a \textit{zoo} object for stock price returns
+for the thirty firms. This is called \textit{StockPriceReturns}
+and another zoo object, \textit{nifty.index}, of market returns.
<<>>=
library(eventstudies)
data(StockPriceReturns)
data(nifty.index)
str(StockPriceReturns)
head(StockPriceReturns)
head(nifty.index)
@
+<<>>= library(eventstudies) data(StockPriceReturns)
+data(nifty.index) str(StockPriceReturns) head(StockPriceReturns)
+head(nifty.index) @
The dates of interest and the firms on which the event occurred are
stored in a data frame, \textit{SplitDates} with two columns
\textit{unit}, the name of the firms, and \textit{when}, the date of
the occurrence of the event. \textit{unit} should be in
\textit{character} format and \textit{when} in \textit{Date} format.
+The dates of interest and the firms on which the event occurred
+are stored in a data frame, \textit{SplitDates} with two columns
+\textit{unit}, the name of the firms, and \textit{when}, the date
+of the occurrence of the event. \textit{unit} should be in
+\textit{character} format and \textit{when} in \textit{Date}
+format.
<<>>=
data(SplitDates)
head(SplitDates)
@
+<<>>= data(SplitDates) head(SplitDates) @
\subsection{Calculating returns}
The function \texttt{excessReturn} calculates the excess returns while
\texttt{marketResidual} calculates the market model. The two inputs
are \texttt{firm.returns} and \texttt{market.returns}. The results are
stored in \texttt{er.result} and \texttt{mm.result} respectively.
+The function \texttt{excessReturn} calculates the excess returns
+while \texttt{marketResidual} calculates the market model. The two
+inputs are \texttt{firm.returns} and \texttt{market.returns}. The
+results are stored in \texttt{er.result} and \texttt{mm.result}
+respectively.
<<>>=
# Excess return
er.result < excessReturn(firm.returns = StockPriceReturns,
 market.returns = nifty.index)
er.result < er.result[rowSums(is.na(er.result))!=NCOL(er.result),]
+<<>>= # Excess return er.result < excessReturn(firm.returns =
+StockPriceReturns, market.returns = nifty.index) er.result <
+er.result[rowSums(is.na(er.result))!=NCOL(er.result),]
head(er.result[,1:3])
@
<<>>=
# Extracting market residual
mm.result < marketResidual(firm.returns = StockPriceReturns,
 market.returns = nifty.index)
mm.result < mm.result[rowSums(is.na(mm.result))!=NCOL(mm.result),]
+@ <<>>= # Extracting market residual mm.result <
+marketResidual(firm.returns = StockPriceReturns, market.returns =
+nifty.index) mm.result <
+mm.result[rowSums(is.na(mm.result))!=NCOL(mm.result),]
head(mm.result[,1:3])
@
+@
The \texttt{AMM} model requires a timeseries of the exchange rate
along with firm returns and market returns. This is done by loading
the \textit{inr} data, which is the INRUSD exchange rate for the same
period. The complete dataset consisting of stock returns, market
returns, and exchange rate is first created.
+along with firm returns and market returns. This is done by
+loading the \textit{inr} data, which is the INRUSD exchange rate
+for the same period. The complete dataset consisting of stock
+returns, market returns, and exchange rate is first created.
The inputs into the \texttt{AMM} model also include
\texttt{firm.returns} and \texttt{market.returns}. Currency returns
can be specified using \texttt{others}. Two types of the AMM model are
supported: \textit{residual} and \textit{all}.
+\texttt{firm.returns} and \texttt{market.returns}. Currency
+returns can be specified using \texttt{others}. Two types of the
+AMM model are supported: \textit{residual} and \textit{all}.
%AMM model
<<>>=
# Create RHS before running AMM()
data(inr)
inrusd < diff(log(inr))*100
all.data < merge(StockPriceReturns,nifty.index,inrusd,all=TRUE)
StockPriceReturns < all.data[,which(colnames(all.data)%in%c("nifty.index",
 "inr"))]
nifty.index < all.data$nifty.index
inrusd < all.data$inr
+% AMM model
+<<>>= # Create RHS before running AMM() data(inr) inrusd <
+diff(log(inr))*100 all.data <
+merge(StockPriceReturns,nifty.index,inrusd,all=TRUE)
+StockPriceReturns <
+all.data[,which(colnames(all.data)%in%c("nifty.index",
+"inr"))] nifty.index < all.data$nifty.index inrusd <
+all.data$inr
## AMM output
## For Full period: dates=NULL
amm.residual < AMM(amm.type="residual",firm.returns=StockPriceReturns[,1:3],
 verbose=FALSE,
 dates= NULL,
 market.returns=nifty.index, others=inrusd,
 switch.to.innov=TRUE, market.returns.purge=TRUE, nlags=0)
+## AMM output ## For Full period: dates=NULL amm.residual <
+AMM(amm.type="residual",firm.returns=StockPriceReturns[,1:3],
+verbose=FALSE, dates= NULL, market.returns=nifty.index,
+others=inrusd, switch.to.innov=TRUE, market.returns.purge=TRUE,
+nlags=0)
amm.output < AMM(amm.type="all",firm.returns=StockPriceReturns[,1:3],
 verbose=FALSE,
 dates= NULL,
 market.returns=nifty.index, others=inrusd,
 switch.to.innov=TRUE, market.returns.purge=TRUE, nlags=1)
+amm.output <
+AMM(amm.type="all",firm.returns=StockPriceReturns[,1:3],
+verbose=FALSE, dates= NULL, market.returns=nifty.index,
+others=inrusd, switch.to.innov=TRUE, market.returns.purge=TRUE,
+nlags=1)
@
+@
\subsection{Conversion to event frame}
For conversion to event time, the event date and the returns on that
date are indexed to 0. Postevent dates are indexed as positive, and
preevent dates as negative. The conversion is done using the
\texttt{phys2eventtime} function. The function requires a returns
series, \textit{StockPriceReturns}, a dataframe with event unit and
time, \textit{SplitDates}, and the width for creating the
eventframe.
+For conversion to event time, the event date and the returns on
+that date are indexed to 0. Postevent dates are indexed as
+positive, and preevent dates as negative. The conversion is done
+using the \texttt{phys2eventtime} function. The function requires
+a returns series, \textit{StockPriceReturns}, a dataframe with
+event unit and time, \textit{SplitDates}, and the width for
+creating the eventframe.
<<>>=
es < phys2eventtime(z=StockPriceReturns, events=SplitDates, width=10)
str(es)
es$outcomes
es.w < window(es$z.e, start=10, end=10)
colnames(es.w) < SplitDates[which(es$outcomes=="success"),1]
SplitDates[1,]
StockPriceReturns[SplitDates[1,2],SplitDates[1,1]]
es.w[,1]
@
+<<>>= es < phys2eventtime(z=StockPriceReturns, events=SplitDates,
+width=10) str(es) es$outcomes es.w < window(es$z.e, start=10,
+end=10) colnames(es.w) <
+SplitDates[which(es$outcomes=="success"),1] SplitDates[1,]
+StockPriceReturns[SplitDates[1,2],SplitDates[1,1]] es.w[,1] @
The output for \texttt{phys2eventtime} is a list. The first element of
a list is a time series object which is converted to event time.
+The output for \texttt{phys2eventtime} is a list. The first
+element of a list is a time series object which is converted to
+event time.
The second element shows the \textit{outcome} of the conversion. If
the outcome is \textit{success} then all is well with the given window
as specified by the width. If there are too many NAs within the event
window, the outcome is \textit{wdatamissing}. The outcome for the
event date not being within the span of data for the unit is
\textit{wrongspan} while the outcome if a unit named in events is not
in the returns data is \textit{unitmissing}.
+The second element shows the \textit{outcome} of the
+conversion. If the outcome is \textit{success} then all is well
+with the given window as specified by the width. If there are too
+many NAs within the event window, the outcome is
+\textit{wdatamissing}. The outcome for the event date not being
+within the span of data for the unit is \textit{wrongspan} while
+the outcome if a unit named in events is not in the returns data
+is \textit{unitmissing}.
In the example described here, es.w contains the returns in eventtime
form for all the stocks. It contains variables for whom all data is
available.
+In the example described here, es.w contains the returns in
+eventtime form for all the stocks. It contains variables for whom
+all data is available.
Once the returns are converted to eventtime, \texttt{remap.cumsum}
function is used to convert the returns to cumulative returns.
+Once the returns are converted to eventtime,
+\texttt{remap.cumsum} function is used to convert the returns to
+cumulative returns.
<<>>=
es.cs < remap.cumsum(es.w,is.pc=FALSE,base=0)
es.cs[,1]
@
+<<>>= es.cs < remap.cumsum(es.w,is.pc=FALSE,base=0) es.cs[,1] @
\subsection{Inference procedures}
\subsubsection{Bootstrap inference}
@@ 317,96 +308,82 @@
The \textit{inference.bootstrap} function does the bootstrap to
generate distribution of $\overline{CR}$. The bootstrap generates
confidence interval at 2.5 percent and 97.5 percent for the estimate.
+confidence interval at 2.5 percent and 97.5 percent for the
+estimate.
<<>>=
result < inference.bootstrap(es.w=es.cs, to.plot=TRUE)
@
+<<>>= result < inference.bootstrap(es.w=es.cs, to.plot=TRUE) @
\begin{figure}[t]
\begin{center}
\caption{Stock splits event and response of respective stock
returns: Bootstrap CI}
\setkeys{Gin}{width=0.8\linewidth}
 \setkeys{Gin}{height=0.8\linewidth}
<<fig=TRUE,echo=FALSE>>=
 result < inference.bootstrap(es.w=es.cs, to.plot=TRUE)
@
\end{center}
\label{fig:one}
+ \setkeys{Gin}{height=0.8\linewidth} <<fig=TRUE,echo=FALSE>>=
+ result < inference.bootstrap(es.w=es.cs, to.plot=TRUE) @
+ \end{center}
+ \label{fig:one}
\end{figure}
\subsubsection{Wilcoxon signed rank test}
We next compute the Wilcoxon signed rank test, which is a
nonparametric inference test to compute the confidence interval.
<<>>=
result < inference.wilcox(es.w=es.cs, to.plot=TRUE)
@
+<<>>= result < inference.wilcox(es.w=es.cs, to.plot=TRUE) @
\begin{figure}[t]
\begin{center}
\caption{Stock splits event and response of respective stock
returns: Wilcoxon CI}
\setkeys{Gin}{width=0.8\linewidth}
 \setkeys{Gin}{height=0.8\linewidth}
<<fig=TRUE,echo=FALSE>>=
 result < inference.wilcox(es.w=es.cs, to.plot=TRUE)
@
\end{center}
\label{fig:two}
+ \setkeys{Gin}{height=0.8\linewidth} <<fig=TRUE,echo=FALSE>>=
+ result < inference.wilcox(es.w=es.cs, to.plot=TRUE) @
+ \end{center}
+ \label{fig:two}
\end{figure}
\subsection{General eventstudy function}
\texttt{eventstudy} is a wrapper around all the internal
functions. Several examples of the use of this function are provided
below.
+functions. Several examples of the use of this function are
+provided below.
<<>>=
## Event study without adjustment
es.na < eventstudy(firm.returns = StockPriceReturns, eventList = SplitDates,
 width = 10, to.remap = TRUE, remap = "cumsum",
 to.plot = TRUE, inference = TRUE,
 inference.strategy = "wilcoxon",
 type = "None")
+<<>>= ## Event study without adjustment es.na <
+eventstudy(firm.returns = StockPriceReturns, eventList =
+SplitDates, width = 10, to.remap = TRUE, remap = "cumsum", to.plot
+= TRUE, inference = TRUE, inference.strategy = "wilcoxon", type =
+"None")
## Event study using market residual and bootstrap
es.mm < eventstudy(firm.returns = StockPriceReturns, eventList = SplitDates,
 width = 10, to.remap = TRUE, remap = "cumsum",
 to.plot = FALSE, inference = TRUE,
 inference.strategy = "bootstrap",
 type = "marketResidual", market.returns = nifty.index)
es.mm
+## Event study using market residual and bootstrap es.mm <
+eventstudy(firm.returns = StockPriceReturns, eventList =
+SplitDates, width = 10, to.remap = TRUE, remap = "cumsum", to.plot
+= FALSE, inference = TRUE, inference.strategy = "bootstrap", type
+= "marketResidual", market.returns = nifty.index) es.mm
## Event study using excess return and bootstrap
es.er < eventstudy(firm.returns = StockPriceReturns, eventList = SplitDates,
 width = 10, to.remap = TRUE, remap = "cumsum",
 to.plot = FALSE, inference = TRUE,
 inference.strategy = "bootstrap",
 type = "excessReturn", market.returns = nifty.index)
+## Event study using excess return and bootstrap es.er <
+eventstudy(firm.returns = StockPriceReturns, eventList =
+SplitDates, width = 10, to.remap = TRUE, remap = "cumsum", to.plot
+= FALSE, inference = TRUE, inference.strategy = "bootstrap", type
+= "excessReturn", market.returns = nifty.index)
## Event study using augmented market model (AMM) and bootstrap
es.amm < eventstudy(firm.returns = StockPriceReturns,
 eventList = SplitDates,
 width = 10, to.remap = TRUE, remap = "cumsum",
 to.plot = FALSE, inference = TRUE,
 inference.strategy = "bootstrap",
 type = "AMM",
 market.returns = nifty.index,
 others=inrusd, verbose=FALSE, dates= NULL,
 switch.to.innov=TRUE, market.returns.purge=TRUE, nlags=1)
+es.amm < eventstudy(firm.returns = StockPriceReturns, eventList =
+SplitDates, width = 10, to.remap = TRUE, remap = "cumsum", to.plot
+= FALSE, inference = TRUE, inference.strategy = "bootstrap", type
+= "AMM", market.returns = nifty.index, others=inrusd,
+verbose=FALSE, dates= NULL, switch.to.innov=TRUE,
+market.returns.purge=TRUE, nlags=1)
@
+@
\section{Computational details}
The package code is purely written in R. It has dependencies to zoo
+The package code is purely written in R. It has dependencies to
+zoo
(\href{http://cran.rproject.org/web/packages/zoo/index.html}{Zeileis
2012}) and boot
(\href{http://cran.rproject.org/web/packages/boot/index.html}{Ripley
 2013}). R itself as well as these packages can be obtained from \href{http://CRAN.Rproject.org/}{CRAN}.
%\section{Acknowledgments}
+ 2013}). R itself as well as these packages can be obtained from
+\href{http://CRAN.Rproject.org/}{CRAN}.
+% \section{Acknowledgments}
%\newpage
\bibliographystyle{jss}
\bibliography{es}
+% \newpage
+\bibliographystyle{jss} \bibliography{es}
\end{document}
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