[Eventstudies-commits] r65 - pkg/vignettes
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed May 1 11:28:43 CEST 2013
Author: vikram
Date: 2013-05-01 11:28:42 +0200 (Wed, 01 May 2013)
New Revision: 65
Added:
pkg/vignettes/eventstudies.bib
Modified:
pkg/vignettes/ees.Rnw
pkg/vignettes/eventstudies.Rnw
Log:
Added references
Modified: pkg/vignettes/ees.Rnw
===================================================================
--- pkg/vignettes/ees.Rnw 2013-04-30 06:29:23 UTC (rev 64)
+++ pkg/vignettes/ees.Rnw 2013-05-01 09:28:42 UTC (rev 65)
@@ -17,8 +17,8 @@
%\VignettePackage{eventstudies}
\maketitle
\begin{abstract}
-The \textit{eventstudies} package also has extreme events
-functionality. This package has \textit{identifyextremeevents}
+The \textit{eventstudies} package includes an extreme events
+functionality. This package has \textit{ees}
function which does extreme event analysis by fusing the
consecutive extreme events in a single event. The methods and
functions are elucidated by employing data-set of S\&P 500 and Nifty.
@@ -26,17 +26,13 @@
\SweaveOpts{engine=R,pdf=TRUE}
\section{Introduction}
-The analysis done using this function is in tandem with Table 1,2,3,4
-and 5 of Patnaik, Shah and Singh (2013). A detail methodology is also
-discussed in the paper mentioned. We use S\&P500 returns to
-understand the \textit{identifyextremeevents} functionality.
-
-Using this function, one can to understand the distribution and run
+Using this function, one can understand the distribution and run
length of the clustered events, quantile values for the extreme
events and yearly distribution of the extreme events. In the sections
-below we replicate the analysis for S\&P 500 from the paper and we
-generate the extreme event study plot for event on S\&P 500 and
-response of NIFTY.
+below we replicate the analysis for S\&P 500 from the Patnaik, Shah
+and Singh (2013) and we generate the extreme event study plot for
+event on S\&P 500 and response of NIFTY. A detail methodology is also
+discussed in the paper.
\section{Extreme event analysis}
This function needs input in returns format on which extreme
@@ -48,14 +44,17 @@
library(eventstudies)
data(eesData)
input <- eesData$sp500
-output <- ees(input, prob.value=5)
+# Suppress messages
+ deprintize<-function(f){
+ return(function(...) {capture.output(w<-f(...));return(w);});
+ }
+output <- deprintize(ees)(input, prob.value=5)
@
% I don't understand this output. Maybe you should explain what it means.
-The output is a list. Primarily it consists of three lists,
-summary statistics for complete data-set, extreme event analysis for
-lower tail and extreme event analysis for upper tail. Further, these
-lower tail and upper tail list objects consists of 5 more list objects with
-following output:
+The output is a list and consists of summary statistics for complete
+data-set, extreme event analysis for lower tail and extreme event
+analysis for upper tail. Further, these lower tail and upper tail list
+objects consists of 5 more list objects with following output:
\begin{enumerate}
\item Extreme events dataset
\item Distribution of clustered and unclustered % events.
@@ -68,7 +67,7 @@
Here we have data summary for the complete data-set which shows
minimum, 5\%, 25\%, median, mean, 75\%, 95\%, maximum, standard
deviation (sd), inter-quartile range (IQR) and number of
-observations. The output shown below mathces with the fourth column
+observations. The output shown below matches with the fourth column
in Table 1 of the paper.
<<>>==
output$data.summary
@@ -130,7 +129,7 @@
The yearly distribution for extreme events include unclustered event
and clustered events which are fused. While in extreme event distribution of
clustered and unclustered event, the clustered events are defined as
-total evnets in a cluster. For example, if there is a clustered event
+total events in a cluster. For example, if there is a clustered event
with three consecutive extreme events then yearly distribution will
treat it as one single event. Here below the relationship between the
Tables is explained through equations:\\\\
@@ -174,10 +173,6 @@
\setkeys{Gin}{height=0.8\linewidth}
<<label=fig12,fig=TRUE,echo=FALSE>>=
<<fig12plot>>
-# Suppress the messages
- deprintize<-function(f){
- return(function(...) {capture.output(w<-f(...));return(w);});
- }
res <- deprintize(eesPlot)(z=eesData, response.series.name="nifty", event.series.name="sp500",titlestring="S&P500", ylab="(Cum.) change in NIFTY", prob.value=5, width=5)
@
\end{center}
@@ -188,7 +183,7 @@
The package code is purely written in R. It has dependencies to zoo
(\href{http://cran.r-project.org/web/packages/zoo/index.html}{Zeileis
2012}) and boot
-(\href{http://cran.r-project.org/web/packages/boot/index.html}{Rlpley
+(\href{http://cran.r-project.org/web/packages/boot/index.html}{Ripley
2013}). R itself as well as these packages can be obtained from \href{http://CRAN.R-project.org/}{CRAN}.
%\section{Acknowledgments}
Modified: pkg/vignettes/eventstudies.Rnw
===================================================================
--- pkg/vignettes/eventstudies.Rnw 2013-04-30 06:29:23 UTC (rev 64)
+++ pkg/vignettes/eventstudies.Rnw 2013-05-01 09:28:42 UTC (rev 65)
@@ -21,19 +21,19 @@
methodology is explained in this paper. In addition to converting
physical dates to event time frame, functions for re-indexing the
event time returns and bootstrap inference estimation. The methods and
-functions are elucidated by employing data-set ofSENSEX firms.
+functions are elucidated by employing data-set of SENSEX firms.
\end{abstract}
\SweaveOpts{engine=R,pdf=TRUE}
\section{Introduction}
-Event study has a long history which dates back to 1933 (James Dolley
-(1933)). It is mostly used to study the response of stock price or
-value of a firm due to events such as mergers \& acquisitions, stock
-splits, quarterly results and so on. It is one of the most widely
-used statistical tool.
+Event study has a long history which dates back to 1938
+\citep{dolley1938effect}). It is mostly used to study the response of
+stock price or value of a firm due to events such as mergers \&
+acquisitions, stock splits, quarterly results and so on. It is one
+of the most widely used statistical tool.
Event study is used to study the response or
-the effect on a variable due to similar events. Efficient and liquid
+the effect on a variable, due to similar events. Efficient and liquid
markets are basic assumption in this methodology. It assumes the
effect on response variable is without delay. As event study output is
further used in econometric analysis, hence significance test such as
@@ -142,10 +142,10 @@
we generate the sampling distribution for the estimate using bootstrap
inference. A detailed explanation of the methodology is presented in
Patnaik, Shah and Singh (2013).
-This specific approach used here is based on Davinson, Hinkley and
-Schectman (1986). The \textit{inference.Ecar} function does the
-bootstrap to generate distribution of $\bar{CR}$. The bootstrap
-generates confidence interval at 2.5\% and 97.5\% for the estimate.
+This specific approach used here is based on
+\citept{davison1986efficient}. The \textit{inference.Ecar} function
+does the bootstrap to generate distribution of $\bar{CR}$. The
+bootstrap generates confidence interval at 2.5\% and 97.5\% for the estimate.
<<>>=
result <- inference.Ecar(z.e=es.cs, to.plot=TRUE)
@@ -168,8 +168,12 @@
The package code is purely written in R. It has dependencies to zoo
(\href{http://cran.r-project.org/web/packages/zoo/index.html}{Zeileis
2012}) and boot
-(\href{http://cran.r-project.org/web/packages/boot/index.html}{Rlpley
+(\href{http://cran.r-project.org/web/packages/boot/index.html}{Ripley
2013}). R itself as well as these packages can be obtained from \href{http://CRAN.R-project.org/}{CRAN}.
%\section{Acknowledgments}
+%\newpage
+\bibliographystyle{jss}
+\bibliography{eventstudies}
+
\end{document}
Added: pkg/vignettes/eventstudies.bib
===================================================================
--- pkg/vignettes/eventstudies.bib (rev 0)
+++ pkg/vignettes/eventstudies.bib 2013-05-01 09:28:42 UTC (rev 65)
@@ -0,0 +1,21 @@
+ at article{dolley1938effect,
+ title={The effect of government regulation in the stock-trading volume of the New York Stock Exchange},
+ author={Dolley, James C},
+ journal={The American Economic Review},
+ volume={28},
+ number={1},
+ pages={8--26},
+ year={1938},
+ publisher={JSTOR}
+}
+
+ at article{davison1986efficient,
+ title={Efficient bootstrap simulation},
+ author={Davinson, AC and Hinkley, David V and Schechtman, E},
+ journal={Biometrika},
+ volume={73},
+ number={3},
+ pages={555--566},
+ year={1986},
+ publisher={Biometrika Trust}
+}
\ No newline at end of file
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