# [Eventstudies-commits] r151 - pkg/vignettes

Tue Oct 29 11:09:24 CET 2013

Author: vikram
Date: 2013-10-29 11:09:24 +0100 (Tue, 29 Oct 2013)
New Revision: 151

Modified:
pkg/vignettes/es.bib
pkg/vignettes/eventstudies.Rnw
Log:
Added volume number to the citation

Modified: pkg/vignettes/es.bib
===================================================================
--- pkg/vignettes/es.bib	2013-10-29 09:26:56 UTC (rev 150)
+++ pkg/vignettes/es.bib	2013-10-29 10:09:24 UTC (rev 151)
@@ -15,12 +15,13 @@
volume = 	 51,
pages = 	 {207-234}}

- at Article{PSS2013,
+ 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/eventstudies.Rnw
===================================================================
--- pkg/vignettes/eventstudies.Rnw	2013-10-29 09:26:56 UTC (rev 150)
+++ pkg/vignettes/eventstudies.Rnw	2013-10-29 10:09:24 UTC (rev 151)
@@ -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{PSS2013}. 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 \citep{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.