The class estimate.mpin is the blueprint of S4 objects that store the results of the estimation of the MPIN model, using the function mpin_ml().

## Usage

# S4 method for estimate.mpin
show(object)

## Arguments

object

an object of class estimate.mpin

## Slots

success

(logical) returns the value TRUE when the estimation has succeeded, FALSE otherwise.

errorMessage

(character) returns an error message if the estimation of the MPIN model has failed, and is empty otherwise.

convergent.sets

(numeric) returns the number of initial parameter sets at which the likelihood maximization converged.

method

(character) returns the method of estimation used, and is equal to 'Maximum Likelihood Estimation'.

layers

(numeric) returns the number of layers detected in the trading data, or provided by the user.

detection

(logical) returns a reference to the layer-detection algorithm used ("E", "EG", "ECM"), if any algorithm is used. If the number of layers is provided by the user, detection takes the value "USER".

parameters

(list) returns the list of the maximum likelihood estimates ($$\alpha$$, $$\delta$$, $$\mu$$, $$\epsilon$$b, $$\epsilon$$s), where $$\alpha$$, $$\delta$$, and $$\mu$$ are numeric vectors of length layers.

aggregates

(numeric) returns an aggregation of information layers' estimated parameters alongside with $$\epsilon$$b, and $$\epsilon$$s. The aggregated parameters are calculated as follows: $$\alpha_{agg} = \sum \alpha_j$$$$\alpha*= \sum \alpha$$j $$\delta_{agg} = \sum \alpha_j \times \delta_j$$$$\delta*= \sum \alpha$$j$$\delta$$j, and $$\mu_{agg} = \sum \alpha_j \times \mu_j$$$$\mu*= \sum \alpha$$j$$\mu$$j.

likelihood

(numeric) returns the value of the (log-)likelihood function evaluated at the optimal set of parameters.

mpinJ

(numeric) returns the values of the multilayer probability of informed trading per layer, calculated using the layer-specific estimated parameters.

mpin

(numeric) returns the global value of the multilayer probability of informed trading. It is the sum of the multilayer probabilities of informed trading per layer stored in the slot mpinJ.

mpin.goodbad

(list) returns a list containing a decomposition of MPIN into good-news, and bad-news MPIN components. The decomposition has been suggested for PIN measure in Brennan et al. (2016) . The list has four elements: mpinG, and mpinB are the global good-news, and bad-news components of MPIN, while mpinGj, and mpinBj are two vectors containing the good-news (bad-news) components of MPIN computed per layer.

dataset

(dataframe) returns the dataset of buys and sells used in the maximum likelihood estimation of the MPIN model.

initialsets

(dataframe) returns the initial parameter sets used in the maximum likelihood estimation of the MPIN model.

details

(dataframe) returns a dataframe containing the estimated parameters of the MLE method for each initial parameter set.

runningtime

(numeric) returns the running time of the estimation of the MPIN model in seconds.