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.

Other Simulation of State Space Models Data Functions: LinSDE2SSM(), LinSDECovEta(), LinSDECovY(), LinSDEMeanEta(), LinSDEMeanY(), ProjectToHurwitz(), ProjectToStability(), SSMCovEta(), SSMCovY(), SSMMeanEta(), SSMMeanY(), SimAlphaN(), SimBetaN(), SimBetaN2(), SimCovDiagN(), SimCovN(), SimNuN(), SimPhiN(), SimPhiN2(), SimSSMFixed(), SimSSMIVary(), SimSSMLinGrowth(), SimSSMLinGrowthIVary(), SimSSMLinSDEFixed(), SimSSMLinSDEIVary(), SimSSMOUFixed(), SimSSMOUIVary(), SimSSMVARFixed(), SimSSMVARIVary(), SpectralRadius(), TestPhi(), TestPhiHurwitz(), 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] -0.01971238  0.01106751 -0.01125932
#> 
#> [[2]]
#> [1] -0.02521992 -0.02068220  0.06565717
#> 
#> [[3]]
#> [1] -0.009554499 -0.020656927 -0.040582313
#> 
#> [[4]]
#> [1]  0.0060266889  0.0358791638 -0.0009399133
#> 
#> [[5]]
#> [1] -0.06423957 -0.04651410  0.01509581
#> 
#> [[6]]
#> [1] -0.02321449  0.06976612  0.01854302
#> 
#> [[7]]
#> [1]  0.108917731 -0.014241541 -0.001112522
#> 
#> [[8]]
#> [1]  0.0212965004 -0.0666725421  0.0001880568
#> 
#> [[9]]
#> [1] 0.007311496 0.002311165 0.010663016
#> 
#> [[10]]
#> [1] 0.04836385 0.01192810 0.03895627
#>