[Eventstudies-commits] r313 - pkg/man

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Fri May 9 19:34:23 CEST 2014


Author: chiraganand
Date: 2014-05-09 19:34:22 +0200 (Fri, 09 May 2014)
New Revision: 313

Modified:
   pkg/man/AMMData.Rd
   pkg/man/EESData.Rd
   pkg/man/NiftyIndex.Rd
   pkg/man/SplitDates.Rd
   pkg/man/StockPriceReturns.Rd
   pkg/man/ees.Rd
   pkg/man/eesPlot.Rd
   pkg/man/eventstudy.Rd
   pkg/man/excessReturn.Rd
   pkg/man/inference.bootstrap.Rd
   pkg/man/inference.wilcox.Rd
   pkg/man/lmAMM.Rd
   pkg/man/makeX.Rd
   pkg/man/manyfirmssubperiod.lmAMM.Rd
   pkg/man/marketResidual.Rd
   pkg/man/phys2eventtime.Rd
   pkg/man/remap.cumprod.Rd
   pkg/man/remap.cumsum.Rd
   pkg/man/remap.event.reindex.Rd
   pkg/man/subperiod.lmAMM.Rd
Log:
Modified language, improved examples.

Modified: pkg/man/AMMData.Rd
===================================================================
--- pkg/man/AMMData.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/AMMData.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -1,20 +1,21 @@
 \name{AMMData}
 \alias{AMMData}
+\docType{data}
 
+\title{Data set containing firm returns, market returns, currency
+  returns, and call money rate used for AMM estimation}
 
-\title{Data containing firm returns, market returns, and currency
-  returns used for AMM estimation}
+\description{This data set consists of daily time series for firm
+  returns (Infosys and TCS), market returns (Nifty returns), currency
+  returns (INR/USD), and call money rate. It is used to demonstrate
+  augmented market model estimation.
 
-\description{The data series is a daily time-series zoo object. The sample range for the data is from 2012-02-01 to 2014-01-31. It consists daily time series for firm returns (Infosys and TCS), market returns (Nifty returns) and currency returns (INR/USD). This data is used to demonstrate the AMM estimation.}
+  The data series is a daily time-series zoo object. The sample range for
+  the data is from 2012-02-01 to 2014-01-31. All series are in per cent.
+}
 
 \usage{data(AMMData)}
 
 \author{Vikram Bahure}
 
-\examples{
-library(zoo)
-data(AMMData)
-str(AMMData)
-}
-
 \keyword{AMMData}

Modified: pkg/man/EESData.Rd
===================================================================
--- pkg/man/EESData.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/EESData.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -1,18 +1,15 @@
 \name{EESData}
-
+\alias{EESData}
 \docType{data}
 
-\alias{EESData}
-
 \title{Returns data used for extreme events analysis}
 
-\description{A daily time series object for S&P 500 and the NIFTY Index.}
+\description{This data set is used to demonstrate extreme events study
+  functionality of the package. It contains daily returns data (in per
+  cent) of S&P 500 and the NIFTY Index.}
 
 \usage{data(EESData)}
 
-\format{\code{zoo}}
+\author{Chirag Anand}
 
-\examples{
-    data(EESData)
-}
 \keyword{datasets}

Modified: pkg/man/NiftyIndex.Rd
===================================================================
--- pkg/man/NiftyIndex.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/NiftyIndex.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -4,14 +4,11 @@
 
 \title{NSE Nifty index from 2004 to 2012}
 
-\description{A sample time series of Nifty index return from 1990 to
-  2012.}
+\description{Time series of Nifty index return (in per cent) from 1990
+  to 2012.}
 
 \usage{data(NiftyIndex)}
 
-\format{\pkg{zoo}}
+\author{Vikram Bahure}
 
-\examples{
-data(NiftyIndex)
-}
-\keyword{datasets}
+\keyword{NiftyIndex}

Modified: pkg/man/SplitDates.Rd
===================================================================
--- pkg/man/SplitDates.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/SplitDates.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -1,12 +1,10 @@
 \name{SplitDates}
-
+\alias{SplitDates}
 \docType{data}
 
-\alias{SplitDates}
+\title{Data set of events used to perform event study analysis}
 
-\title{Sample data containing set of events to perform eventstudy analysis.}
-
-\description{ This data set contains stock split event dates for the index
+\description{This data set contains stock split event dates for the index
   constituents of the Bombay Stock Exchange index (SENSEX). The data
   format follows the required format in the function
   \code{phys2eventtime}, with two columns 'outcome.unit' (firm name) and
@@ -14,9 +12,6 @@
 
 \usage{data(SplitDates)}
 
-\format{\code{data.frame}}
+\author{Vikram Bahure}
 
-\examples{
-    data(SplitDates)
-}
 \keyword{datasets}

Modified: pkg/man/StockPriceReturns.Rd
===================================================================
--- pkg/man/StockPriceReturns.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/StockPriceReturns.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -1,20 +1,15 @@
 \name{StockPriceReturns}
-
+\alias{StockPriceReturns}
 \docType{data}
 
-\alias{StockPriceReturns}
+\title{Stock price returns data}
 
-\title{Sample data containing stock price returns}
+\description{This data set contains stock price returns (in per cent) of
+  30 major stocks on the National Stock Exchange (NSE) of India for a
+  period of 23 years.}
 
-\description{This data set contains stock price returns of 30 major
-  stocks on the National Stock Exchange (NSE) of India for a period of 23
-  years.}
-
 \usage{data(StockPriceReturns)}
 
-\format{\code{zoo}}
+\author{Vikram Bahure}
 
-\examples{
-    data(StockPriceReturns)
-}
 \keyword{datasets}

Modified: pkg/man/ees.Rd
===================================================================
--- pkg/man/ees.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/ees.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -80,21 +80,12 @@
     \url{http://onlinelibrary.wiley.com/doi/10.1111/j.1468-2362.2013.12032.x/abstract}
     \url{http://macrofinance.nipfp.org.in/releases/PatnaikShahSingh2013_Foreign_Investors.html}
   }
-
-  To convert number to words, code uses function
-  \href{http://finzi.psych.upenn.edu/R/Rhelp02a/archive/46843.html}{\dQuote{numbers2words}}
-  by \href{http://socserv.mcmaster.ca/jfox/}{John Fox} and
-  \dQuote{deprintize} function by \href{http://mbq.me/}{Miron Kursa}.
 }
 
 \author{Vikram Bahure, Vimal Balasubramaniam}
 
 \examples{
-library(eventstudies)
 data(EESData)
-## Input S&P 500 as the univariate series
-input <- EESData$sp500
-## Constructing summary statistics for 5% tail values (5% on both sides)
-output <- ees(input, prob.value = 5)
-str(output)
+r <- ees(EESData$sp500, prob.value = 5)
+str(r, max.level = 2)
 }

Modified: pkg/man/eesPlot.Rd
===================================================================
--- pkg/man/eesPlot.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/eesPlot.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -69,9 +69,7 @@
 \author{Vikram Bahure, Vimal Balasubramaniam}
 
 \examples{
-library(eventstudies)
 data(EESData)
-## Generating event study plots (using modified event study methodology)
 eesPlot(z = EESData,
         response.series.name = "nifty",
         event.series.name = "sp500",

Modified: pkg/man/eventstudy.Rd
===================================================================
--- pkg/man/eventstudy.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/eventstudy.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -202,8 +202,6 @@
 }
 
 \examples{ 
-## Performing event study
-library(eventstudies)
 data("StockPriceReturns")
 data("SplitDates")
 

Modified: pkg/man/excessReturn.Rd
===================================================================
--- pkg/man/excessReturn.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/excessReturn.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -27,7 +27,6 @@
 data(StockPriceReturns)
 data(NiftyIndex)
 
-## Excess return
 er.result <- excessReturn(firm.returns = StockPriceReturns,
 			  market.returns = NiftyIndex)
 

Modified: pkg/man/inference.bootstrap.Rd
===================================================================
--- pkg/man/inference.bootstrap.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/inference.bootstrap.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -30,7 +30,7 @@
   }
 
   \item{to.plot}{a \sQuote{logical} indicating whether to generate an
-    eventstudy plot of the inference estimated. Defaults to
+    event study plot of the inference estimated. Defaults to
     \sQuote{TRUE}.
   }
 
@@ -61,7 +61,7 @@
 \examples{
 data(StockPriceReturns)
 data(SplitDates)
-## Converting physical dates to event time frame
+
 es.results <- phys2eventtime(z = StockPriceReturns,
                              events = SplitDates,
                              width = 5)
@@ -69,10 +69,7 @@
                start = -5,
                end = +5)
 
-## Cumulating event window
 eventtime <- remap.cumsum(es.w, is.pc = FALSE, base = 0)
-
-## Constructing confidence interval using bootstrap inference strategy
 inference.bootstrap(es.w = eventtime,
                     to.plot = FALSE)
 }

Modified: pkg/man/inference.wilcox.Rd
===================================================================
--- pkg/man/inference.wilcox.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/inference.wilcox.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -27,7 +27,7 @@
   }
 
   \item{to.plot}{a \sQuote{logical} indicating whether to generate an
-    eventstudy plot of the inference estimated. Defaults to
+    event study plot of the inference estimated. Defaults to
     \sQuote{TRUE}.
   }
   
@@ -55,14 +55,12 @@
 \examples{
 data(StockPriceReturns)
 data(SplitDates)
-## Converting physical dates to event time frame
+
 es.results <- phys2eventtime(z = StockPriceReturns,
                              events = SplitDates,
                              width = 5)
 es.w <- window(es.results$z.e, start = -5, end = +5)
-
-## Cumulating event window
 eventtime <- remap.cumsum(es.w, is.pc = FALSE, base = 0)
-## Constructing confidence interval using wilcoxon inference strategy
+
 inference.wilcox(es.w = eventtime, to.plot = FALSE)
 }

Modified: pkg/man/lmAMM.Rd
===================================================================
--- pkg/man/lmAMM.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/lmAMM.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -85,14 +85,12 @@
 market.returns <- AMMData[,"index.nifty"]
 currency.returns <- AMMData[,"currency.inrusd"]
 
-## Creating regressors for AMM estimation using makeX function
 X <- makeX(market.returns,
           others = currency.returns,
           switch.to.innov = FALSE,
           market.returns.purge = FALSE,
           verbose = FALSE)
 
-## Augmented market model residual
 amm.result <- lmAMM(firm.returns, X, nlags = 0, verbose = FALSE)
 plot(amm.result)
 

Modified: pkg/man/makeX.Rd
===================================================================
--- pkg/man/makeX.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/makeX.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -79,7 +79,6 @@
 market.returns <- AMMData$index.nifty
 currency.returns <- AMMData$currency.inrusd
 
-## Constructing regressors (independent variables) for AMM
 X <- makeX(market.returns,
            others = currency.returns,
            switch.to.innov = FALSE,

Modified: pkg/man/manyfirmssubperiod.lmAMM.Rd
===================================================================
--- pkg/man/manyfirmssubperiod.lmAMM.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/manyfirmssubperiod.lmAMM.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -59,16 +59,12 @@
 }
 
 \examples{
-
-## Running manyfirmssubperiod.lmAMM() involves as many steps as working
-## with onefirmAMM.
 data("AMMData")
 
 firm.returns <- AMMData[, c("Infosys","TCS")]
 market.returns <- AMMData[, "index.nifty"]
 currency.returns <- AMMData[, "currency.inrusd"]
 
-## Creating X for AMM estimation using makeX function
 X <- makeX(market.returns,
            others = currency.returns,
            nlags = 1,
@@ -77,7 +73,6 @@
            verbose = FALSE,
 	   dates = as.Date(c("2012-02-01", "2013-01-01", "2014-01-20")))
 
-## Estimating exposure
 res <- manyfirmssubperiod.lmAMM(firm.returns = firm.returns,
                                 X = X,
                                 lags = 1,

Modified: pkg/man/marketResidual.Rd
===================================================================
--- pkg/man/marketResidual.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/marketResidual.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -24,7 +24,6 @@
 data(StockPriceReturns)
 data(NiftyIndex)
 
-## Market model residual
 mm.result <- marketResidual(firm.returns = StockPriceReturns,
                             market.returns = NiftyIndex)
 

Modified: pkg/man/phys2eventtime.Rd
===================================================================
--- pkg/man/phys2eventtime.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/phys2eventtime.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -78,7 +78,6 @@
 data(StockPriceReturns)
 data(SplitDates)
 
-## Converting physical dates to event time
 result <- phys2eventtime(z = StockPriceReturns,
 			 events = SplitDates,
 			 width = 5)

Modified: pkg/man/remap.cumprod.Rd
===================================================================
--- pkg/man/remap.cumprod.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/remap.cumprod.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -37,19 +37,16 @@
 data(StockPriceReturns)
 data(SplitDates)
 
-## Converting to event time frame
 es.results <- phys2eventtime(z = StockPriceReturns,
                              events = SplitDates,
                              width = 5)
 es.w <- window(es.results$z.e, start = -5, end = +5)
 
-## Cumulating (geometric product) event window output
 eventtime <- remap.cumprod(es.w,
                            is.pc = TRUE,
                            is.returns = TRUE,
                            base = 100)
 
-## Comparing abnormal returns (AR) and cumulative (geometric) abnormal returns (CAR)
 check.output <- cbind(es.w[,1], eventtime[,1])
 colnames(check.output) <- c("abnormal.returns", "cumulative.abnormal.returns")
 check.output

Modified: pkg/man/remap.cumsum.Rd
===================================================================
--- pkg/man/remap.cumsum.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/remap.cumsum.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -39,13 +39,10 @@
 data(StockPriceReturns)
 data(SplitDates)
 
-## Converting to event time frame
 es.results <- phys2eventtime(z = StockPriceReturns,
                              events = SplitDates,
                              width = 5)
 es.w <- window(es.results$z.e, start = -5, end = +5)
-
-## Cumulating (arithmetic) event window output
 eventtime <- remap.cumsum(es.w, is.pc = FALSE, base = 0)
 
 ## Comparing abnormal returns (AR) and cumulative abnormal returns (CAR)

Modified: pkg/man/remap.event.reindex.Rd
===================================================================
--- pkg/man/remap.event.reindex.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/remap.event.reindex.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -29,13 +29,12 @@
 data(StockPriceReturns)
 data(SplitDates)
 
-## Converting to event time frame
 es.results <- phys2eventtime(z = StockPriceReturns,
                              events = SplitDates,
                              width = 5)
 es.w <- window(es.results$z.e, start = -5, end = +5)
 
-## Reindexing event time (t=0) to 100
 eventtime <- remap.event.reindex(es.w)
-head(eventtime[,1:5])
+
+head(eventtime[, 1:5])
 }

Modified: pkg/man/subperiod.lmAMM.Rd
===================================================================
--- pkg/man/subperiod.lmAMM.Rd	2014-05-02 18:15:53 UTC (rev 312)
+++ pkg/man/subperiod.lmAMM.Rd	2014-05-09 17:34:22 UTC (rev 313)
@@ -67,12 +67,10 @@
 \examples{ 
 data("AMMData")
 
-## Create RHS before running subperiod.lmAMM()
 firm.returns <- AMMData$Infosys
 market.returns <- AMMData$index.nifty
 currency.returns <- AMMData$currency.inrusd
 
-## Constructing regressors for AMM
 regressors <- makeX(market.returns,
                     others = currency.returns,
                     switch.to.innov = TRUE,
@@ -81,7 +79,6 @@
                     dates = as.Date(c("2012-02-01","2013-01-01","2014-01-20")),
                     verbose = FALSE)
 
-## Run AMM for one firm across different periods
 res <- subperiod.lmAMM(firm.returns,
                        X = regressors,
                        nlags = 1,



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