The class estimate.mpin.ecm
is the blueprint of
S4
objects that store the results of the estimation of the MPIN
model using the Expectation-Conditional Maximization method, as
implemented in the function mpin_ecm()
.
Usage
# S4 method for estimate.mpin.ecm
show(object)
selectModel(object, criterion)
# S4 method for estimate.mpin.ecm
selectModel(object, criterion)
getSummary(object)
# S4 method for estimate.mpin.ecm
getSummary(object)
Arguments
- object
an object of class
estimate.mpin.ecm
.- criterion
a character string specifying the model selection criterion.
criterion
should take one of these values{"BIC", "AIC", "AWE"}
. They stand for Bayesian Information Criterion, Akaike Information Criterion, and Approximate Weight of Evidence, respectively.
Functions
selectModel,estimate.mpin.ecm-method
: returns the optimal model among the estimated models, i.e., the model having the lowest information criterion, provided by the user.getSummary,estimate.mpin.ecm-method
: returns a summary of the estimation of theMPIN
model using theECM
algorithm for different values of the argumentlayers
. For each estimation, the number of layers, theMPIN
value, the log-likelihood value, as well as the values of the different information criteria, namelyAIC
,BIC
andAWE
are displayed.
Slots
success
(
logical
) returns the valueTRUE
when the estimation has succeeded,FALSE
otherwise.errorMessage
(
character
) returns an error message if theMPIN
estimation 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, and is equal to 'Expectation-Conditional Maximization Algorithm'.layers
(
numeric
) returns the number of layers estimated by the Expectation-Conditional Maximization algorithm, or provided by the user.optimal
(
logical
) returns whether the number of layers used for the estimation is provided by the user(optimal=FALSE)
, or determined by theECM
algorithm(optimal=TRUE)
.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 lengthlayers
.aggregates
(
numeric
) returns an aggregation of information layers' 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 slotmpinJ
.mpin.goodbad
(
list
) returns a list containing a decomposition ofMPIN
into good-news, and bad-newsMPIN
components. The decomposition has been suggested for PIN measure in Brennan et al. (2016) . The list has four elements:mpinG
, andmpinB
are the global good-news, and bad-news components ofMPIN
, whilempinGj
, andmpinBj
are two vectors containing the good-news (bad-news) components ofMPIN
computed per layer.dataset
(
dataframe
) returns the dataset of buys and sells used in the ECM estimation of the MPIN model.initialsets
(
dataframe
) returns the initial parameter sets used in the ECM estimation of the MPIN model.details
(
dataframe
) returns a dataframe containing the estimated parameters of theECM
method for each initial parameter set.models
(
list
) returns the list ofestimate.mpin.ecm
objects storing the results of estimation using the functionmpin_ecm()
for different values of the argumentlayers
. It returnsNULL
when the argumentlayers
of the functionmpin_ecm()
take a specific value.AIC
(
numeric
) returns the value of the Akaike Information Criterion (AIC).BIC
(
numeric
) returns the value of the Bayesian Information Criterion (BIC).AWE
(
numeric
) returns the value of the Approximate Weight of Evidence.criterion
(
character
) returns the model selection criterion used to find the optimal estimate for theMPIN
model. It takes one of these values'BIC'
,'AIC'
,'AWE'
; which stand for Bayesian Information Criterion, Akaike Information Criterion, and Approximate Weight of Evidence, respectively.hyperparams
(
list
) returns the hyperparameters of theECM
algorithm, which areminalpha
,maxeval
,tolerance
, andmaxlayers
. Check the details section ofmpin_ecm()
to know more about these parameters.runningtime
(
numeric
) returns the running time of the estimation in seconds.