Description Usage Arguments Details Value Model arguments Author(s) See Also Examples
‘eventstudy’ provides an easy interface that integrates all functionalities of package eventstudies to undertake event study analysis. It allows the user to specify the type of data adjustment to be done (using market model functionalities of the package) and then an inference strategy of choice.
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firm.returns 
a zoo matrix of ‘outcome’ or ‘response’ series. 
event.list 
a 
event.window 
an ‘integer’ of length 1 that specifies a
symmetric event window around the event time as specified in the

type 
a scalar of type ‘character’ specifying the type of data adjustment required before conducting an event study analysis. See ‘Details’. 
to.remap 
‘logical’, indicating whether or not to remap the data in ‘firm.returns’. 
remap 
‘character’, indicating the type of remap required, “cumsum”, “cumprod”, or “reindex”. Used when ‘to.remap’ is ‘TRUE’. 
is.levels 
‘logical’, indicating whether data in ‘firm.returns’ needs to be converted into percentage returns. If ‘TRUE’, ‘firm.returns’ will be converted into percentage returns. 
inference 
‘logical’, specifying whether to undertake statistical inference and compute confidence intervals. The default setting is ‘TRUE’. 
inference.strategy 
a ‘character’ scalar specifying the inference strategy to be used for estimating the confidence interval. Presently, two methods are available: “bootstrap” and “wilcox”. 
model.args 
All other arguments to be passed depends on whether ‘type’ is “marketModel”, “excessReturn”, or “lmAMM”. When “None”, no additional arguments will be needed. See ‘Details’. 
This function is used to conduct event study analysis acting as a wrapper over the functionality provided in the eventstudies package. It provides an interface to select and control the process of event study analysis. It includes choice of the statistical model for doing insample estimation and computing coefficients, choice of cumulative returns, and selection of inference procedure. Process used to conduct a study is detailed below:
event.period: is defined as (event.window, event.window].
estimation.period: If “type” is specified, then estimation.period is calculated for each firmevent in “event.list”, starting from the start of the data span till the start of event period (inclusive).
For each firmevent, firm returns and other returns (as applicable) are converted to event time using ‘phys2eventtime’. Data is merged using ‘merge.zoo’ to make sure the indexes are consistent before conversion to event time.
The selected model “type” is run on the series indexed by event time and abnormal returns are computed.
NULL values because of estimation data missing are removed from the output and “outcomes” object is updated with “edatamissing”.
Remapping is done if “to.remap” is ‘TRUE’ using the function specified in “remap” argument.
Means of returns are computed across various events.
Inference is done if “inference” is ‘TRUE’ using the technique specified in “inference.strategy”.
“firm.returns” can contain a single series also. To study a single
series, use ‘[’ with drop = FALSE
to subset the
data set. See phys2eventtime
for more details.
‘NA’ values in the returns data are converted to 0
.
“type” currently supports:
“marketModel”: uses marketModel
function to regress market returns on firms return using a
linear model.
“excessReturn”: uses excessReturn
to
subtract market return from firm return.
“lmAMM”: uses lmAMM
to perform
Augmented Market Model estimation.
“None”: does not use any model.
Arguments to a model type can be sent inside ‘model.args’. See ‘Model arguments’ section for details on accepted fields.
“remap” can take three values:
“cumsum”: cumulative sum, uses remap.cumsum
. [Default]
“cumprod”: cumulative product, buyholdabnormalreturn (BHAR),
uses remap.cumprod
.
“reindex”: reindexes the event window by using
remap.event.reindex
.
For computing confidence intervals, the function can either use
bootstrap or Wilcoxon signedrank test. See
inference.bootstrap
and inference.wilcox
for more details.
‘model.args’ is directly supplied to the model mentioned in the “type” argument. See section on ‘Model arguments’ for more details.
Note: phys2eventtime
is called with ‘width’ set
to 0 when called from this function.
A list with class attribute “es” holding the following elements, or ‘NULL’ if output from a model function is ‘NULL’:
“eventstudy.output”: a ‘matrix’ containing mean (bootstrap) or median (with wilcox) estimate with confidence interval; ‘NULL’ if there are no “success” “outcomes”.
“outcomes”:
a character vector that is the output from
phys2eventtime
containing details of the successful use
of an event:
success: shows the successful use of event date.
wdatamissing: appears when width data is missing around the
event. This will not appear when this function is used since the
argument ‘width’ in phys2eventtime
is set to 0.
wrongspan: if event date falls outside the range of physical date.
unitmissing: when the unit (firm name) is missing in the event list.
edatamissing: when there is insufficient data to do model estimation.
The returned object contains input information in other attributes:
“model.residuals”: a ‘list’ of residual series as returned by the selected model. For models which do not compute residuals, this attribute is not returned.
“CAR”: a ‘zoo’ object contaning Cumulative Abnormal Returns as returned by the function specified by “remap” argument.
“inference”: a ‘character’ providing information about which inference strategy was utilised to estimate the confidence intervals.
“inference.strategy”: a ‘character’ providing the name of the selected model.
“event.window”: a ‘numeric’ specifying the window width for event study output.
“remap”: a ‘character’ specifying the remapping technique used. Options are mentioned in “remap” argument description.
Function ‘print.es’ is provided to print the coefficients and exposures of the analysis. ‘plot.es’ is used to plot the model residuals and firm returns.
Each model can take extra arguments (supplied as ‘model.args’) apart from mandatory ones for finer control over the analysis. Check the respective function documentation for definitions. The arguments from the relevant functions are listed here:
“marketModel”:
 market.returns
“excessReturn”:
 market.returns
“lmAMM”:
 market.returns
 others
 switch.to.innov
 market.returns.purge
 nlag.makeX
 nlag.lmAMM
 dates
 verbose
Note: arguments (except nlag.lmAMM) are directly passed to ‘makeX’, see
lmAMM
for more details.
Ajay Shah, Chirag Anand, Vikram Bahure, Vimal Balasubramaniam
lmAMM
,
marketModel
,
excessReturn
,
phys2eventtime
,
inference.bootstrap
,
inference.wilcox
,
remap.cumsum
,
remap.cumprod
,
remap.event.reindex
,
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data("StockPriceReturns")
data("SplitDates")
data("OtherReturns")
# Event study without adjustment
es < eventstudy(firm.returns = StockPriceReturns,
event.list = SplitDates,
event.window = 7,
type = "None",
to.remap = TRUE,
remap = "cumsum",
inference = TRUE,
inference.strategy = "bootstrap")
str(es)
plot(es)
# Event study using Market Model
es < eventstudy(firm.returns = StockPriceReturns,
event.list = SplitDates,
event.window = 7,
type = "marketModel",
to.remap = TRUE,
remap = "cumsum",
inference = TRUE,
inference.strategy = "bootstrap",
model.args = list(
market.returns = OtherReturns[, "NiftyIndex"]
)
)
str(es)
plot(es)
# Event study using Augmented Market Model
es < eventstudy(firm.returns = StockPriceReturns,
event.list = SplitDates,
event.window = 7,
type = "lmAMM",
to.remap = TRUE,
remap = "cumsum",
inference = TRUE,
inference.strategy = "bootstrap",
# model arguments
model.args = list(
market.returns = OtherReturns[, "NiftyIndex"],
others = OtherReturns[, "USDINR"],
market.returns.purge = TRUE,
nlag.makeX = 5,
nlag.lmAMM = 5
)
)
str(es)
plot(es)

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