mtarsim {BMTAR} R Documentation

## Multivariate threshold autoregressive process simulation

### Description

Given an list object of the class “regime” (length = l) with the model specification simulates N observations for a MTAR (Multivariate threshold autoregressive process) process.

### Usage

mtarsim(N, Rg, r = NULL, Xt = NULL, Zt = NULL, seed = NULL)


### Arguments

 N numeric type greater than 0. Number of observation to simulate. Not NULL Rg list type object of length l number of regimes of the process with names (R1, ..., Rl), each a class “regime” object. Not NULL r numeric type of length l - 1, threshold value (within the range of Z_t). Default NULL Xt matrix (Nx\nu) type object, covariate process (admit NA values). Default NULL Zt matrix (Nx1) type object, threshold process (admit NA values). Default NULL seed numeric type, set a seed for simulation

Given a list of length l of object of class “regime” (model specification), it simulates observations of a MTAR process ($Sim) and returns them an object of the class “mtarsim”. We have an MTAR process is given by:  Y_t= \Phi_{0}^(j)+\sum_{i=1}^{p_j}\Phi_{i}^{(j)} Y_{t-i}+\sum_{i=1}^{q_j} \beta_{i}^{(j)} X_{t-i} + \sum_{i=1}^{d_j} \delta_{i}^{(j)} Z_{t-i}+ \Sigma_{(j)}^{1/2} \epsilon_{t}  if r_{j-1}< Z_t \leq r_{j} The simulation has 100 burn observations to stabilize the process. It is possible to simulate univariate (TAR, SETAR, etc.) or multivariate (VAR) processes, properly specifying the regime type object according to the model. ### Value Return a list type object of class “mtarsim”:  Sim object class “tsregime” Reg list type object with names (R1, ..., Rl) each one class “regime” pj vector of autoregressive orders in each regime qj vector of covariate lags orders in each regime dj vector of lags orders of threshold process in each regime ### Author(s) Valeria Bejarano vbejaranos@unal.edu.co, Sergio Calderon sacalderonv@unal.edu.co & Andrey Rincon adrincont@unal.edu.co ### References Calderon, S. and Nieto, F. (2017) Bayesian analysis of multivariate threshold autoregress models with missing data. Communications in Statistics - Theory and Methods 46 (1):296–318. doi:10.1080/03610926.2014.990758. ### See Also mtaregime, mtarns, mtarstr, mtarmissing, mtarnumreg ### Examples ## get Ut data process Tlen = 500 Sigma_ut = 2 Phi_ut = list(phi1 = 0.3) R_ut = list(R1 = mtaregime(orders = list(p = 1,q = 0,d = 0),Phi = Phi_ut,Sigma = Sigma_ut)) Ut = mtarsim(N = Tlen,Rg = R_ut,seed = 124) Zt = Ut$Sim$Yt # Yt process k = 2 ## R1 regime Phi_R1 = list(phi1 = matrix(c(0.1,0.6,-0.4,0.5),k,k,byrow = TRUE)) Sigma_R1 = matrix(c(1,0,0,1),k,k,byrow = TRUE) R1 = mtaregime(orders = list(p = 1,q = 0,d = 0),Phi = Phi_R1,Sigma = Sigma_R1) ## R2 regime Phi_R2 = list(phi1 = matrix(c(0.3,0.5,0.2,0.7),2,2,byrow = TRUE)) Sigma_R2 = matrix(c(2.5,0.5,0.5,1),2,2,byrow = TRUE) R2 = mtaregime(orders = list(p = 1,q = 0,d = 0), Phi = Phi_R2,Sigma = Sigma_R2) ## create list of regime-type objects Rg = list(R1 = R1,R2 = R2) r = 0.3 # get the simulation datasim = mtarsim(N = Tlen,Rg = Rg,r = r,Zt = Zt,seed = 124) autoplot.tsregime(datasim$Sim,1)
autoplot.tsregime(datasim\$Sim,2)


[Package BMTAR version 0.1.1 Index]