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()`

.

## Slots

`success`

(

`logical`

) takes the value`TRUE`

when the estimation has succeeded,`FALSE`

otherwise.`errorMessage`

(

`character`

) contains an error message if the`PIN`

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 the`PIN`

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 of`PIN`

into good-news, and bad-news`PIN`

components. The decomposition has been suggested in Brennan et al. (2016) . The list has two elements:`pinG`

, and`pinB`

are the good-news, and bad-news components of`PIN`

, 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 the`MLE`

method for each initial parameter set.`runningtime`

(

`numeric`

) returns the running time of the estimation of the`PIN`

model in seconds.