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 estimate.pin
show(object)

## Arguments

object

an object of class estimate.pin

## 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.