# [Eventstudies-commits] r152 - pkg/vignettes

Tue Oct 29 11:29:55 CET 2013

Author: vikram
Date: 2013-10-29 11:29:55 +0100 (Tue, 29 Oct 2013)
New Revision: 152

pkg/vignettes/ees.bib
Modified:
pkg/vignettes/ees.Rnw
pkg/vignettes/es.bib
pkg/vignettes/eventstudies.Rnw
Log:
Added bib entry for Extreme event study (ees) analysis and some minor correction

Modified: pkg/vignettes/ees.Rnw
===================================================================
--- pkg/vignettes/ees.Rnw	2013-10-29 10:09:24 UTC (rev 151)
+++ pkg/vignettes/ees.Rnw	2013-10-29 10:29:55 UTC (rev 152)
@@ -17,10 +17,10 @@
\maketitle

\begin{abstract}
-One specific application of the eventstudies package is Patnaik, Shah and Singh (2013) % TODO: Bibliography please.
+One specific application of the eventstudies package is \citet{PatnaikShahSingh2013}.
in this document. The function \texttt{ees} is a wrapper available in the package for
users to undertake similar extreme-events'' analysis.
-We replicate the published work of Patnaik, Shah and Singh (2013) % TODO: bibtex please
+We replicate the published work of \citet{PatnaikShahSingh2013}
and explore this wrapper in detail in this document.
\end{abstract}

@@ -32,7 +32,7 @@

There are several concerns with an extreme-event analysis. Firstly, what happens when multiple tail events (Clustered events'') occur within one another? We facilitate this analysis with summary statistics on the distribution and run length of events, quantile values to determine tail events'', and yearly distribution of the extreme-events. Secondly, do results change when we use clustered events'' and unclustered events'' separately, or, together in the same analysis? This wrapper also facilitates such sensitivity analysis in the study of extreme-events.

-In the next few sections, we replicate one sub-section of results from Patnaik, Shah and Singh (2013) % TODO: bibtex citation.
+In the next few sections, we replicate one sub-section of results from \citet{PatnaikShahSingh2013}
that studies whether extreme events on the S\&P 500 affects returns on the domestic Indian stock market measured by the Nifty Index. A detailed mathematical overview of the methodology is also available in the paper.

@@ -83,6 +83,7 @@
The overall dataset looks as follows:

<<>>=
+head(output$lower.tail$data)
str(output$lower.tail$data)
@

@@ -150,5 +151,6 @@
2013}).  R itself as well as these packages can be obtained from \href{http://CRAN.R-project.org/}{CRAN}.

%\section{Acknowledgments}
+\bibliographystyle{jss} \bibliography{ees}

\end{document}

===================================================================
--- pkg/vignettes/ees.bib	                        (rev 0)
+++ pkg/vignettes/ees.bib	2013-10-29 10:29:55 UTC (rev 152)
@@ -0,0 +1,10 @@
+ at Article{PatnaikShahSingh2013,
+  author = 	 {Patnaik, Ila and Shah, Ajay and Singh, Nirvikar},
+  title = 	 {Foreign Investors Under Stress: Evidence from India },
+  journal = 	 {International Finance},
+  year = 	 2013,
+volume =         16,
+number= 2,
+pages = {213-244}
+}
+

Modified: pkg/vignettes/es.bib
===================================================================
--- pkg/vignettes/es.bib	2013-10-29 10:09:24 UTC (rev 151)
+++ pkg/vignettes/es.bib	2013-10-29 10:29:55 UTC (rev 152)
@@ -25,9 +25,9 @@
pages = {213-244}
}

- at article{dvison1986efficient,
+ at article{davison1986efficient,
title={Efficient bootstrap simulation},
-  author={DVISON, AC and Hinkley, DV and Schechtman, E},
+  author={Davinson, AC and Hinkley, DV and Schechtman, E},
journal={Biometrika},
volume={73},
number={3},

Modified: pkg/vignettes/eventstudies.Rnw
===================================================================
--- pkg/vignettes/eventstudies.Rnw	2013-10-29 10:09:24 UTC (rev 151)
+++ pkg/vignettes/eventstudies.Rnw	2013-10-29 10:29:55 UTC (rev 152)
@@ -234,7 +234,7 @@
While the package is sufficiently generalised to undertake a wide array of inference procedures, at present it contains only two inference procedures: 1/ The bootstrap and 2/ Wilcoxon Rank test. We look at both in turn below:

\subsubsection{Bootstrap inference}
-We hold an event time object that contains several cross-sectional observations for a single definition of an event: The stock split. At each event time, i.e., $-T,-(T-1),...,0,...,(T-1),T$, we hold observations for 30 stocks. At this point, without any assumption on the distribution of these cross sectional returns, we can generate the sampling distribution for the location estimator (mean in this case) using non-parametric inference procedures. The bootstrap is our primary function in the suite of inference procedures under construction.\footnote{Detaild explanation of the methodology is presented in \citep{PatnaikShahSingh2013}. This specific approach is based on \citet{davison1986efficient}.}
+We hold an event time object that contains several cross-sectional observations for a single definition of an event: The stock split. At each event time, i.e., $-T,-(T-1),...,0,...,(T-1),T$, we hold observations for 30 stocks. At this point, without any assumption on the distribution of these cross sectional returns, we can generate the sampling distribution for the location estimator (mean in this case) using non-parametric inference procedures. The bootstrap is our primary function in the suite of inference procedures under construction.\footnote{Detaild explanation of the methodology is presented in \citet{PatnaikShahSingh2013}. This specific approach is based on \citet{davison1986efficient}.}

\textit{inference.bootstrap} performs the bootstrap to generate distribution of $\overline{CR}$. The bootstrap generates confidence interval at 2.5 percent and 97.5 percent for the estimate.