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(), SSMCovEta(), SSMCovY(), SSMMeanEta(), SSMMeanY(), 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] 0.0303682339 0.0264968137 0.0004621614
#> 
#> [[2]]
#> [1] -0.01423570 -0.03370255  0.01462578
#> 
#> [[3]]
#> [1] -0.01332358 -0.03574804  0.01346426
#> 
#> [[4]]
#> [1] -0.02434587  0.01219682  0.03603814
#> 
#> [[5]]
#> [1] -0.026119255  0.048167892  0.006704065
#> 
#> [[6]]
#> [1] -0.03109606  0.07280703 -0.03827979
#> 
#> [[7]]
#> [1] 0.027558781 0.006540289 0.037142750
#> 
#> [[8]]
#> [1]  0.04284297 -0.01569974  0.02092334
#> 
#> [[9]]
#> [1] -0.010518354 -0.022416387 -0.002043949
#> 
#> [[10]]
#> [1] -0.004537512 -0.016906034  0.004346010
#>