# [Eventstudies-commits] r154 - pkg/vignettes

Wed Oct 30 04:22:47 CET 2013

Author: vikram
Date: 2013-10-30 04:22:45 +0100 (Wed, 30 Oct 2013)
New Revision: 154

Modified:
pkg/vignettes/eventstudies.Rnw
Log:
Minor correction in spelling

Modified: pkg/vignettes/eventstudies.Rnw
===================================================================
--- pkg/vignettes/eventstudies.Rnw	2013-10-29 13:34:37 UTC (rev 153)
+++ pkg/vignettes/eventstudies.Rnw	2013-10-30 03:22:45 UTC (rev 154)
@@ -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 \citet{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{Detailed 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.