Description Usage Arguments Details Value Author(s) References Examples

This function performs event study analysis on extreme event dates (‘eesDates’) and using formatted output (‘get.clusters.formatted’)

1 2 | ```
eesInference(input, event.lists, event.window, to.remap = TRUE, remap = "cumsum",
inference = "TRUE", inference.strategy = "bootstrap")
``` |

`input` |
a formatted cluster object, as returned by ‘get.clusters.formatted’ function. |

`event.lists` |
a ‘list’ of normal and purged events as returned by ‘eesDates’. |

`event.window` |
an ‘integer’ of length 1 that specifies a symmetric event window around the event date. |

`to.remap` |
‘logical’, indicating whether or not to remap the data in ‘input’.The default setting is ‘TRUE’ |

`remap` |
‘character’, indicating the type of remap required, “cumsum”, “cumprod”, or “reindex”. Used when ‘to.remap’ is ‘TRUE’. |

`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”. The default setting is ‘bootstrap’. |

This function performs event study analysis using
`eventstudy`

function on the extreme event dates of normal
(unclustered events) and purged (clustered and unclustered events)
sets. These interesting dates are obtained from function ‘eesDates’. The
function can estimate confidence interval using different inference
strategies as provided by `eventstudy()`

.

The function does not do market model adjustment but takes the
output of `get.clusters.formatted`

as it's input.

Format of event study output is a ‘matrix’ containing
mean or median estimate with confidence interval; ‘NULL’ if
there are no “success” “outcomes”. See
`phys2eventtime`

for more details.

A ‘list’ with class attribute “ees” holding the following four event study output elements:

`good.normal` |
an event study inference ‘matrix’ for right tail unclustered events, termed as normal |

`bad.normal` |
an event study inference ‘matrix’ for left tail unclustered events, termed as normal |

`good.purged` |
an event study inference ‘matrix’ for right tail clustered and unclustered events, termed as purged |

`bad.purged` |
an event study inference ‘matrix’ for left tail clustered and unclustered events, termed as purged |

Vikram Bahure, Chirag Anand

Ila Patnaik, Nirvikar Singh and Ajay Shah (2013). Foreign Investors under stress: Evidence from India. International Finance, 16(2), 213-244. http://onlinelibrary.wiley.com/doi/10.1111/j.1468-2362.2013.12032.x/abstract http://macrofinance.nipfp.org.in/releases/PatnaikShahSingh2013_Foreign_Investors.html

1 2 3 4 5 6 7 8 9 10 11 | ```
data(OtherReturns)
formattedClusters <- get.clusters.formatted(event.series = OtherReturns[, "SP500"],
response.series = OtherReturns[, "NiftyIndex"])
event.lists <- eesDates(formattedClusters)
inference <- eesInference(input = formattedClusters,
event.lists = event.lists,
event.window = 5)
str(inference, max.level = 2)
``` |

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