MPIN estimation results — estimate.mpin-class • PINstimation

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.