# sim_BivariateTrawl: Simulates a bivariate trawl process In trawl: Estimation and Simulation of Trawl Processes

## Description

Simulates a bivariate trawl process

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```sim_BivariateTrawl(t, Delta = 1, burnin = 10, marginal = base::c("Poi", "NegBin"), dependencetype = base::c("fullydep", "dep"), trawl1 = base::c("Exp", "DExp", "supIG", "LM"), trawl2 = base::c("Exp", "DExp", "supIG", "LM"), v1 = 0, v2 = 0, v12 = 0, kappa1 = 0, kappa2 = 0, kappa12 = 0, a1 = 0, a2 = 0, lambda11 = 0, lambda12 = 0, w1 = 0, delta1 = 0, gamma1 = 0, alpha1 = 0, H1 = 0, lambda21 = 0, lambda22 = 0, w2 = 0, delta2 = 0, gamma2 = 0, alpha2 = 0, H2 = 0) ```

## Arguments

 `t` parameter which specifying the length of the time interval [0,t] for which a simulation of the trawl process is required. `Delta` parameter Δ specifying the length of the time step, the default is 1 `burnin` parameter specifying the length of the burn-in period at the beginning of the simulation `marginal` parameter specifying the marginal distribution of the trawl `dependencetype` Parameter specifying the type of dependence `trawl1` parameter specifying the type of the first trawl function `trawl2` parameter specifying the type of the second trawl function `v1, v2, v12` parameters of the Poisson distribution `kappa1, kappa2, kappa12, a1, a2` parameters of the (possibly bivariate) negative binomial distribution `lambda11, lambda12, w1` parameters of the exponential (or double-exponential) trawl function of the first process `delta1, gamma1` parameters of the supIG trawl function of the first process `alpha1, H1` parameter of the long memory trawl of the first process `lambda21, lambda22, w2` parameters of the exponential (or double-exponential) trawl function of the second process `delta2, gamma2` parameters of the supIG trawl function of the second process `alpha2, H2` parameter of the long memory trawl of the second process

## Details

This function simulates a bivariate trawl process with either Poisson or negative binomial marginal law. For the trawl function there are currently four choices: exponential, double-exponential, supIG or long memory. More details on the precise simulation algorithm is available in the vignette.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```#Simulate a bivariate negative binomial trawl process with exponential trawl #functions #Parameters of the exponential trawls: lambda1 <- 1.2 lambda2 <- 1.5 #Parameters of the negative binomial marginal law: m1 <- 2.1 theta1 <- 0.9 a1 <- 27.3 m2 <- 2.3 theta2 <- 0.9 a2 <- 35.3 kappa12 <- m1 kappa1 <- 0 kappa2 <- m2 - kappa12 #Specify the time period and grid t <- 720 Delta <- 1 #Fix the seed set.seed(1) #Simulate the bivariate trawl process with common factor #and independent components ("dep") and negative binomial # marginal law. Both trawl functions are chosen as exponentials. simdata <- sim_BivariateTrawl(t, Delta, burnin=10,marginal ="NegBin", dependencetype="dep", trawl1 ="Exp", trawl2 ="Exp", kappa1=kappa1,kappa2=kappa2,kappa12=kappa12,a1=a1,a2=a2,lambda11=lambda1, lambda21 =lambda2) ```

trawl documentation built on Aug. 16, 2018, 5:04 p.m.