fit.seMPP {QRMlib}R Documentation

Fit Marked Self-Exciting Point Process

Description

fits marked self-exciting process to a point process object of class MPP

Usage

fit.seMPP(PP, markdens = "GPD", model = "Hawkes", mark.influence = TRUE, 
predictable = FALSE, std.errs = FALSE)

Arguments

PP a point process object of class MPP
markdens name of density of mark distribution; currently must be "GPD"
model name of self-exciting model: Hawkes or ETAS
mark.influence whether marks of marked point process may influence the self-excitement
predictable whether previous events may influence the scaling of mark distribution
std.errs whether standard errors should be computed VALUE

Details

see pages 307-309 of QRM

Value

a fitted self-exciting process object of class sePP

See Also

fit.sePP, plot.sePP, stationary.sePP

Examples

data(sp500);
sp500.nreturns <- -mk.returns(sp500); 
window <- (seriesPositions(sp500.nreturns) > 
            timeDate("12/31/1995",format = "%m/%d/%Y")); 
sp500.nreturns <- sp500.nreturns[window]; 
tmp <- extremalPP(sp500.nreturns,ne=100); 
mod3a <- fit.seMPP(tmp,mark.influence=FALSE,std.errs=TRUE);
## Not run: 
 
mod3b <- fit.seMPP(tmp,mark.influence=TRUE,std.errs=TRUE); 
mod3c <- fit.seMPP(tmp,model="ETAS",mark.influence=FALSE,std.errs=TRUE); 
mod3d <- fit.seMPP(tmp,model="ETAS",mark.influence=TRUE,std.errs=TRUE); 
mod4a <- fit.seMPP(tmp,mark.influence=FALSE,predictable=TRUE,
           std.errs=TRUE); 
mod4b <- fit.seMPP(tmp,mark.influence=TRUE,predictable=TRUE,
         std.errs=TRUE); 
mod4c <- fit.seMPP(tmp,model="ETAS",mark.influence=FALSE,
                   predictable=TRUE,std.errs=TRUE); 
mod4d <- fit.seMPP(tmp,model="ETAS",mark.influence=TRUE,
                   predictable=TRUE,std.errs=TRUE);
## End(Not run)

[Package QRMlib version 1.4 Index]