random_coefmats2: Create random stationary VAR model (dxd) coefficient matrices...

In saviviro/gmvarkit: Estimate Gaussian and Student's t Mixture Vector Autoregressive Models

Create random stationary VAR model (dxd) coefficient matrices A.

Description

random_coefmats2 generates random VAR model coefficient matrices.

Usage

random_coefmats2(p, d, ar_scale = 1)

Arguments

p	a positive integer specifying the autoregressive order of the model.
d	the number of time series in the system.
ar_scale	a positive real number between zero and one. Larger values will typically result larger AR coefficients.

Details

The coefficient matrices are generated using the algorithm proposed by Ansley and Kohn (1986) which forces stationarity. It's not clear in detail how ar_scale affects the coefficient matrices. Read the cited article by Ansley and Kohn (1986) and the source code for more information.

Note that when using large ar_scale with large p or d, numerical inaccuracies caused by the imprecision of the float-point presentation may result in errors or nonstationary AR-matrices. Using smaller ar_scale facilitates the usage of larger p or d.

Value

Returns ((pd^2)x1) vector containing stationary vectorized coefficient matrices (vec(A_{1}),...,vec(A_{p}).

References

• Ansley C.F., Kohn R. 1986. A note on reparameterizing a vector autoregressive moving average model to enforce stationarity. Journal of statistical computation and simulation, 24:2, 99-106.

saviviro/gmvarkit documentation built on March 8, 2024, 4:15 a.m.