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(), SimBetaNCovariate(), SimCovDiagN(), SimCovN(), SimNuN(), SimPhiN(), SimPhiN2(), SimPhiNCovariate(), 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] -0.01612411  0.05248648 -0.06911157
#> 
#> [[2]]
#> [1] 0.062837536 0.004901415 0.042171823
#> 
#> [[3]]
#> [1]  0.040770037 -0.026217983  0.008281761
#> 
#> [[4]]
#> [1] -0.04008987  0.04394645 -0.02669320
#> 
#> [[5]]
#> [1] 0.005175554 0.004878003 0.014484618
#> 
#> [[6]]
#> [1]  0.005608641  0.028381893 -0.022871889
#> 
#> [[7]]
#> [1] 0.014337823 0.021865137 0.007149303
#> 
#> [[8]]
#> [1] -0.021604931 -0.001307357 -0.048671658
#> 
#> [[9]]
#> [1] -0.02821143  0.00963608  0.04793082
#> 
#> [[10]]
#> [1] -0.006411421  0.002284498  0.064332877
#>