Simulate Intercept Vectors in a Continuous-Time Vector Autoregressive Model from the Multivariate Normal Distribution

This function simulates random intercept vectors in a continuous-time vector autoregressive model from the multivariate normal distribution.

Usage

SimIotaN(n, iota, vcov_iota_l)

Arguments

n

Positive integer. Number of replications.

iota

Numeric vector. Intercept (\(\boldsymbol{\iota}\)).

vcov_iota_l

Numeric matrix. Cholesky factorization (t(chol(vcov_iota))) of the sampling variance-covariance matrix of \(\boldsymbol{\iota}\).

Value

Returns a list of random intercept vectors.

See also

Other Simulation of State Space Models Data Functions: LinSDE2SSM(), LinSDECov(), LinSDEMean(), SSMCov(), SSMMean(), SimAlphaN(), SimBetaN(), SimCovDiagN(), SimCovN(), SimPhiN(), SimSSMFixed(), SimSSMIVary(), SimSSMLinGrowth(), SimSSMLinGrowthIVary(), SimSSMLinSDEFixed(), SimSSMLinSDEIVary(), SimSSMOUFixed(), SimSSMOUIVary(), SimSSMVARFixed(), SimSSMVARIVary(), TestPhi(), TestStability(), TestStationarity()

Author

Ivan Jacob Agaloos Pesigan

Examples

n <- 10
iota <- c(0, 0, 0)
vcov_iota_l <- t(chol(0.001 * diag(3)))
SimIotaN(n = n, iota = iota, vcov_iota_l = vcov_iota_l)
#> [[1]]
#>              [,1]
#> [1,] 0.0303682339
#> [2,] 0.0264968137
#> [3,] 0.0004621614
#> 
#> [[2]]
#>             [,1]
#> [1,] -0.01423570
#> [2,] -0.03370255
#> [3,]  0.01462578
#> 
#> [[3]]
#>             [,1]
#> [1,] -0.01332358
#> [2,] -0.03574804
#> [3,]  0.01346426
#> 
#> [[4]]
#>             [,1]
#> [1,] -0.02434587
#> [2,]  0.01219682
#> [3,]  0.03603814
#> 
#> [[5]]
#>              [,1]
#> [1,] -0.026119255
#> [2,]  0.048167892
#> [3,]  0.006704065
#> 
#> [[6]]
#>             [,1]
#> [1,] -0.03109606
#> [2,]  0.07280703
#> [3,] -0.03827979
#> 
#> [[7]]
#>             [,1]
#> [1,] 0.027558781
#> [2,] 0.006540289
#> [3,] 0.037142750
#> 
#> [[8]]
#>             [,1]
#> [1,]  0.04284297
#> [2,] -0.01569974
#> [3,]  0.02092334
#> 
#> [[9]]
#>              [,1]
#> [1,] -0.010518354
#> [2,] -0.022416387
#> [3,] -0.002043949
#> 
#> [[10]]
#>              [,1]
#> [1,] -0.004537512
#> [2,] -0.016906034
#> [3,]  0.004346010
#>