estimate.pin-class {PINstimation} | R Documentation |
PIN estimation results
Description
The class estimate.pin
is a blueprint of S4
objects
that store the results of the different PIN
functions: pin()
, pin_yz()
,
pin_gwj()
, and pin_ea()
.
Usage
## S4 method for signature 'estimate.pin'
show(object)
Arguments
object |
an object of class |
Slots
success
(
logical
) takes the valueTRUE
when the estimation has succeeded,FALSE
otherwise.errorMessage
(
character
) contains an error message if thePIN
estimation has failed, and is empty otherwise.convergent.sets
(
numeric
) returns the number of initial parameter sets at which the likelihood maximization converged.algorithm
(
character
) returns the algorithm used to determine the set of initial parameter sets for the maximum likelihood estimation. It takes one of the following values:-
"YZ"
: Yan and Zhang (2012) -
"GWJ"
: Gan, Wei and Johnstone (2015) -
"YZ*"
: Yan and Zhang (2012) as modified by Ersan and Alici (2016) -
"EA"
: Ersan and Alici (2016) -
"CUSTOM"
: Custom initial parameter sets
-
factorization
(
character
) returns the factorization of thePIN
likelihood function as used in the maximum likelihood estimation. It takes one of the following values:-
"NONE"
: No factorization -
"EHO"
: Easley, Hvidkjaer and O'Hara (2010) -
"LK"
: Lin and Ke (2011) -
"E"
: Ersan (2016)
-
parameters
(
list
) returns the list of the maximum likelihood estimates (\alpha
,\delta
,\mu
,\epsilon
b,\epsilon
s)likelihood
(
numeric
) returns the value of (the factorization of) the likelihood function evaluated at the optimal set of parameters.pin
(
numeric
) returns the value of the probability of informed trading.pin.goodbad
(
list
) returns a list containing a decomposition ofPIN
into good-news, and bad-newsPIN
components. The decomposition has been suggested in Brennan et al. (2016). The list has two elements:pinG
, andpinB
are the good-news, and bad-news components ofPIN
, respectively.dataset
(
dataframe
) returns the dataset of buys and sells used in the maximum likelihood estimation of the PIN model.initialsets
(
dataframe
) returns the initial parameter sets used in the maximum likelihood estimation of the PIN model.details
(
dataframe
) returns a dataframe containing the estimated parameters by theMLE
method for each initial parameter set.runningtime
(
numeric
) returns the running time of the estimation of thePIN
model in seconds.