The class estimate.adjpin is a blueprint of the S4
objects that store the results of the estimation of the AdjPIN model using
adjpin().
# S4 method for estimate.adjpin
show(object)(estimate.adjpin-class)
success(logical) takes the value TRUE when the estimation has
succeeded, FALSE otherwise.
errorMessage(character) contains an error message if the estimation
of the AdjPIN model has failed, and is empty otherwise.
convergent.sets(numeric) returns the number of initial parameter
sets, for which the likelihood maximization converged.
method(character) contains a reference to the estimation method:
"ECM" for expectation-conditional maximization algorithm and '"ML"'
for standard maximum likelihood estimation.
factorization(character) contains a reference to the factorization
of the likelihood function used: "GE"for the factorization in
Ersan2022b;textualPINstimation, and "NONE" for the
original likelihood function in Duarte09;textualPINstimation.
restrictions(list) returns a binary list that contains the set of
parameter restrictions on the original AdjPIN model in the estimated AdjPIN
model. The restrictions are imposed equality constraints on model parameters.
If the value of the parameter restricted is the empty list (list()),
then the model has no restrictions, and the estimated model is the
unrestricted, i.e., the original AdjPIN model. If not empty, the list
contains one or multiple of the following four elements
{theta, mu, eps, d}. For instance, If theta is set to TRUE,
then the estimated model has assumed the equality of the probability of
liquidity shocks in no-information, and information days, i.e.,
\(\theta\)=\(\theta'\). If any of the remaining rate elements
{mu, eps, d} is equal to TRUE, (say mu=TRUE), then the
estimated model imposed equality of the concerned parameter on the buy
side, and on the sell side (\(\mu\)b=\(\mu\)s). If more than one element is
equal to TRUE, then the restrictions are combined. For instance,
if the slot restrictions contains list(theta=TRUE, eps=TRUE, d=TRUE),
then the estimated AdjPIN model has three restrictions \(\theta\)=\(\theta'\),
\(\epsilon\)b=\(\epsilon\)s, and \(\Delta\)b=\(\Delta\)s, i.e., it has been estimated with just 7
parameters, in comparison to 10 in the original unrestricted model.
algorithm(character) returns the implemented initial parameter
set determination algorithm. "GE" is for
Ersan2022b;textualPINstimation,
"CL" is for ChengLai2021;textualPINstimation,
"RANDOM" for random initial parameter sets, and "CUSTOM" for
custom initial parameter sets.
parameters(numeric) returns the vector of the optimal
maximum-likelihood estimates ( \(\alpha\), \(\delta\), \(\theta\),
\(\theta'\), \(\epsilon\)b, \(\epsilon\)s, \(\mu\)b, \(\mu\)s, \(\Delta\)b, \(\Delta\)s).
likelihood(numeric) returns the value (of the factorization) of the
likelihood function, as in Ersan2022b;textualPINstimation,
evaluated at the set of optimal parameters.
adjpin(numeric) returns the value of the adjusted probability of
informed trading Duarte09PINstimation.
psos(numeric) returns the probability of symmetric order flow shock
Duarte09PINstimation.
dataset(dataframe) returns the dataset of buys and sells used
in the estimation of the AdjPIN model.
initialsets(dataframe) returns the initial parameter sets used
in the estimation of AdjPIN model.
details(dataframe) returns a dataframe containing the estimated
parameters for each initial parameter set.
hyperparams(list) returns the hyperparameters of the ECM
algorithm, which are maxeval, and tolerance.
runningtime(numeric) returns the running time of the AdjPIN
estimation in seconds.