arimasim {paramsim}

Parameterized Simulation

Description

Parameterized Simulation

Usage

arimasim(
  a,
  z,
  n,
  ar11,
  ma11,
  ar22,
  ma22,
  ar33,
  ma33,
  p,
  d,
  q,
  sd = sd,
  j1,
  k1,
  j2,
  k2,
  j3,
  k3,
  arr1,
  maa1,
  arr2,
  maa2,
  arr3,
  maa3
)

Arguments

a

first seed boundary

z

last seed boundary

n

number of samples

ar11

character to search for in third coefficient of autoregressive

ma11

character to search for in third coefficient of autoregressive

ar22

character to search for in third coefficient of autoregressive

ma22

character to search for in third coefficient of autoregressive

ar33

character to search for in third coefficient of autoregressive

ma33

character to search for in third coefficient of autoregressive

p

order of the autoregressive

d

degree of difference

q

degree of moving average

sd

standard deviation of the series

j1

length of character to search for in first coefficient of autoregressive

k1

length of character to search for in third coefficient of autoregressive

j2

length of character to search for in second coefficient of autoregressive

k2

length of character to search for in third coefficient of autoregressive

j3

length of character to search for in third coefficient of autoregressive

k3

length of character to search for in third coefficient of autoregressive

arr1

character to search for in first coefficient of autoregressive

maa1

character to search for in third coefficient of autoregressive

arr2

character to search for in second coefficient of autoregressive

maa2

character to search for in third coefficient of autoregressive

arr3

character to search for in third coefficient of autoregressive

maa3

character to search for in third coefficient of autoregressive

Value

A data frame get printed to the console with its first colomn being the rank and the next few column could be the coefficients of AR or MA both with varying orders depending on the order and classes of ARIMA model being searched for. The last column of the data frame could be the intercept if any exist within the range of the search.

Functions

• arimasim(): arimasim helps to Search for rigth seeds for the rigth AR simulation with arima.sin() finction using auto.arima() function

  Search for rigth seeds for the rigth ARIMA simulation with arima.sin() function using auto.arima() function

  This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. Consider the case of reproducing a time series data set of size 20 that uses an autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1. When one checks the arima.sin() function's estimated parameters, it's possible that after a single trial or a few more, one won't find the precise parameters. This enables one to look for the ideal RNG setting for a simulation that will accurately duplicate the desired parameters.

Examples

  arimasim(a= 289805,z= 289806,n= 10,p= 1,d= 0,q= 0,ar11= 0.8,sd = 1,j1= 4,arr1= "0.80")