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(), LinSDEInterceptEta(), LinSDEInterceptY(), LinSDEMeanEta(), LinSDEMeanY(), ProjectToHurwitz(), ProjectToStability(), SSMCovEta(), SSMCovY(), SSMInterceptEta(), SSMInterceptY(), SSMMeanEta(), SSMMeanY(), SimAlphaN(), SimBetaN(), SimBetaN2(), SimBetaNCovariate(), SimCovDiagN(), SimCovN(), SimMVN(), SimMuN(), 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] 0.01854302 0.04752510 0.10891773
#> 
#> [[2]]
#> [1] -0.001112522  0.011419870  0.021296500
#> 
#> [[3]]
#> [1]  0.0001880568 -0.0254260549  0.0073114955
#> 
#> [[4]]
#> [1] 0.0106630163 0.0007951237 0.0483638530
#> 
#> [[5]]
#> [1]  0.03895627  0.01945489 -0.04111851
#> 
#> [[6]]
#> [1]  0.037935872 -0.004766437  0.013510904
#> 
#> [[7]]
#> [1] 0.02954862 0.04305909 0.02114061
#> 
#> [[8]]
#> [1] 0.008611313 0.035027771 0.017578026
#> 
#> [[9]]
#> [1] -0.01612411  0.05248648 -0.06911157
#> 
#> [[10]]
#> [1] 0.062837536 0.004901415 0.042171823
#>