sim.marx: The simulation of MARX processes

In MARX: Simulation, Estimation, Model Selection and Forecasting for MARX Models

Description

This function allows you to simulate MARX processes based on different underlying distribution.

Usage

sim.marx(dist.eps, dist.x, obs, c_par, nc_par, exo_par)

Arguments

dist.eps	vector containing the error distribution and its parameters (options: t, normal, stable).
dist.x	vector containing the distribution of x and its parameters (options: t, normal, stable). Specify NULL or "not" if not wanted.
obs	Number of observations for simulated process.
c_par	vector of causal parameters.
nc_par	vector of noncausal parameters.
exo_par	Parameter of the exogenous variable.

Value

y	Simulated data y.
x	Simulated data x (exogenous variable).

Author(s)

Sean Telg

Examples

dist.eps <- c('t',1,1) ## t-distributed errors with 1 degree of freedom and scale parameter 1
dist.x   <- c('normal',0,1) ## standard normally distributed x variable
obs <- 100
c_par <- c(0.2,0.4)
nc_par <- 0.8
exo_par <- 0.5
sim.marx(dist.eps,dist.x,obs,c_par,nc_par,exo_par) ## Simulates a MARX(2,1,1) process

Example output

$y
  [1]  57.70826399  60.71268953  44.31973097  45.03002379  42.10631401
  [6]  45.04393618  50.41463408  57.96439382  56.13657649  64.70738906
 [11]  73.17807571  89.66028631  49.20552928  51.11964575  36.20136872
 [16]  30.52849611  24.21784547  22.51624113  19.44752937  14.39660716
 [21]  13.31884110  16.00230598  18.19635493  28.12660176  31.90269589
 [26]  41.47627907  49.21509850  61.54973584  78.59110779  96.61435915
 [31] 117.66186149 143.81051685 176.15250743 100.98038152  98.50707674
 [36]  69.18006256  56.92465192  43.03768681  37.11208389  26.38319291
 [41]  23.89279997  20.03423217  18.30739638  18.28485568  13.79881771
 [46]   8.45742446   5.99050936   3.67242830   2.06164605   1.20585137
 [51]  -0.14946545   0.08696855  -2.52436299  -2.41578219  -4.62763896
 [56]  -4.84644841  -7.68942591  -9.80398007 -13.54699903 -15.84717963
 [61]  -9.57836906 -13.84414176 -12.14093607 -18.56028569 -21.52334305
 [66] -10.76731936 -13.18525300 -11.60180651 -16.49360345 -15.27593268
 [71] -21.11126802 -22.83289465 -29.64838969 -33.45080377 -21.05101910
 [76] -22.23139071 -17.20525908 -16.56986468 -13.15579244 -12.70584965
 [81] -11.91538022  -9.52736495  -8.94588766  -7.60694791  -8.36241103
 [86]   0.73639851   0.32160178   6.39332846   9.38802099  15.50356651
 [91]   8.46828581   9.08214916   8.36417799   7.89197873   8.26555239
 [96]   6.95836715   3.77579226   5.72893853   0.38331733   3.84894331

$x
  [1] -1.26955196 -1.06300673 -1.71241868 -0.46010022 -0.10258792  0.17122895
  [7]  3.28215892 -1.51967886  0.33293492 -0.21515447 -1.91715138  0.47852010
 [13]  0.57799276 -0.53564032  0.94638411 -0.88610033 -0.05044124  1.27383491
 [19] -1.12826962  0.50366505 -0.88710221  0.37140649 -0.13223392  0.43352186
 [25]  0.98732923  1.35046062 -1.09361760 -0.54888240  0.66042560  0.13989968
 [31]  1.19636031 -0.35927417 -0.21562215  0.16227156 -0.18538721 -1.72286748
 [37] -0.79844114  0.37285401  0.59163539 -1.46875944  0.35013159 -0.46633919
 [43] -1.25915399 -1.17073333 -1.24593797 -0.64868222 -0.44150184 -2.00376326
 [49]  0.10824788  0.27405233 -1.39811431  0.76886019  0.08359211 -0.02402492
 [55] -1.86070825  1.15467647  0.63569617  0.91760624 -1.82261441  1.45232185
 [61]  1.28128920  0.10014904  1.09168343  0.95755309 -0.93358993  0.14865132
 [67]  0.01015553  0.21735992 -1.66692327  2.16788346  0.58971850  1.66375626
 [73] -1.77396086  1.59687727  0.35669409 -0.38365228  1.63068238  1.31416995
 [79] -0.07373302 -0.47100992 -0.44591724 -0.69407700 -0.81177395 -0.56552446
 [85] -0.33794411 -0.11452747 -1.25350848  0.04483264 -1.11756084  0.04040616
 [91]  1.13956171 -0.11382763  2.19258601 -1.03643379  0.47969073  0.33763595
 [97] -0.34416028 -0.27836373 -0.32496433  0.41861042

MARX documentation built on May 2, 2019, 3:42 a.m.