Simulate Intercept Vectors in a Discrete-Time Vector Autoregressive Model from the Multivariate Normal Distribution
Source:R/RcppExports.R
SimAlphaN.Rd
This function simulates random intercept vectors in a discrete-time vector autoregressive model from the multivariate normal distribution.
See also
Other Simulation of State Space Models Data Functions:
LinSDE2SSM()
,
LinSDECovEta()
,
LinSDECovY()
,
LinSDEMeanEta()
,
LinSDEMeanY()
,
ProjectToHurwitz()
,
ProjectToStability()
,
SSMCovEta()
,
SSMCovY()
,
SSMMeanEta()
,
SSMMeanY()
,
SimBetaN()
,
SimBetaN2()
,
SimCovDiagN()
,
SimCovN()
,
SimIotaN()
,
SimPhiN()
,
SimPhiN2()
,
SimSSMFixed()
,
SimSSMIVary()
,
SimSSMLinGrowth()
,
SimSSMLinGrowthIVary()
,
SimSSMLinSDEFixed()
,
SimSSMLinSDEIVary()
,
SimSSMOUFixed()
,
SimSSMOUIVary()
,
SimSSMVARFixed()
,
SimSSMVARIVary()
,
SpectralRadius()
,
TestPhi()
,
TestPhiHurwitz()
,
TestStability()
,
TestStationarity()
Examples
n <- 10
alpha <- c(0, 0, 0)
vcov_alpha_l <- t(chol(0.001 * diag(3)))
SimAlphaN(n = n, alpha = alpha, vcov_alpha_l = vcov_alpha_l)
#> [[1]]
#> [1] -0.020575349 0.001385859 0.035564236
#>
#> [[2]]
#> [1] -0.04451615 0.03050132 -0.02610542
#>
#> [[3]]
#> [1] 0.00295405 0.00641909 -0.01880957
#>
#> [[4]]
#> [1] 0.0638962949 -0.0004094139 0.0385942726
#>
#> [[5]]
#> [1] -0.009690720 0.006371313 0.011313655
#>
#> [[6]]
#> [1] -0.01283227 -0.06108355 -0.04104300
#>
#> [[7]]
#> [1] -0.001098922 0.048238135 -0.028804885
#>
#> [[8]]
#> [1] -0.01763396 -0.00591308 0.02505656
#>
#> [[9]]
#> [1] 0.02516893 -0.05903032 0.01244699
#>
#> [[10]]
#> [1] 0.020485834 0.001943539 0.011134900
#>