ACD_Fit: Function for Estimation of an ACD Model

In fACD: Autoregressive Conditional Duration Models in R

Description

This function estimates, based on maximum likelihood, the parameters of an ACD (autoregressive conditional duration) model.

Usage

ACD_Fit(x, qLag = 1, pLag = 1, distrib = "exp", typeACD = "ACD")

Arguments

x	Vector with observed durations
qLag	Maximum lag for alpha parameter
pLag	Maximum lag for beta parameter
distrib	Assumed distribution ('exp' or 'weibull')
typeACD	Functional form of conditional duration equation (possible value = 'ACD','log','BC' or 'EX')

Value

Returns a S4 object with the following slots:

x	Observed Durations
qLag	Maximum lag for alpha parameter
pLag	Maximum lag for beta parameter
condDur	Conditional Duration Series
Coeff	A list with all estimated coefficients
Coeff_Std	A list with all standard errors for coefficients
Coeff_pValues	A list with all p values of coefficients
LL	Value of maximum log likelihood
paramVec	A vector with all coefficients (same values as in Coeff)
nParameter	Number of parameters in the model
sizeModel	A list with the size of the model (number of indep var, etc)
distrib	Assumed distribution for ML estimation
typeACD	Assumed functional form of ACD filter
timeRun	Time of estimation of model

Author(s)

Marcelo Perlin - ICMA/UK <[e-mail]>

See Also

ACD_Simul,acdModel-class

Examples

data(myDur)


x=myDur
pLag=1
qLag=1
distrib='exp'

typeACD='ACD'

modelOut<-ACD_Fit(x,qLag,pLag,distrib,typeACD)

fACD documentation built on May 2, 2019, 6:10 p.m.