is_stationary: Check the stationary condition of a given GMVAR, StMVAR, or...

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is_stationaryR Documentation

Check the stationary condition of a given GMVAR, StMVAR, or G-StMVAR model


is_stationary checks the stationarity condition of a GMVAR, StMVAR, or G-StMVAR model.


  all_boldA = NULL,
  structural_pars = NULL,
  tolerance = 0.001



a positive integer specifying the autoregressive order of the model.

For GMVAR and StMVAR models:

a positive integer specifying the number of mixture components.

For G-StMVAR models:

a size (2x1) integer vector specifying the number of GMVAR type components M1 in the first element and StMVAR type components M2 in the second element. The total number of mixture components is M=M1+M2.


the number of time series in the system.


a real valued vector specifying the parameter values.

For reduced form models:

Should be size ((M(pd^2+d+d(d+1)/2+2)-M1-1)x1) and have the form \theta = (\upsilon_{1}, ...,\upsilon_{M}, \alpha_{1},...,\alpha_{M-1},\nu), where

  • \upsilon_{m} = (\phi_{m,0},\phi_{m},\sigma_{m})

  • \phi_{m} = (vec(A_{m,1}),...,vec(A_{m,p})

  • and \sigma_{m} = vech(\Omega_{m}), m=1,...,M,

  • \nu=(\nu_{M1+1},...,\nu_{M})

  • M1 is the number of GMVAR type regimes.

For structural model:

Should have the form \theta = (\phi_{1,0},...,\phi_{M,0},\phi_{1},...,\phi_{M}, vec(W),\lambda_{2},...,\lambda_{M},\alpha_{1},...,\alpha_{M-1},\nu), where

  • \lambda_{m}=(\lambda_{m1},...,\lambda_{md}) contains the eigenvalues of the mth mixture component.

Above, \phi_{m,0} is the intercept parameter, A_{m,i} denotes the ith coefficient matrix of the mth mixture component, \Omega_{m} denotes the error term covariance matrix of the m:th mixture component, and \alpha_{m} is the mixing weight parameter. The W and \lambda_{mi} are structural parameters replacing the error term covariance matrices (see Virolainen, 2022). If M=1, \alpha_{m} and \lambda_{mi} are dropped. If parametrization=="mean", just replace each \phi_{m,0} with regimewise mean \mu_{m}. vec() is vectorization operator that stacks columns of a given matrix into a vector. vech() stacks columns of a given matrix from the principal diagonal downwards (including elements on the diagonal) into a vector.

In the GMVAR model, M1=M and \nu is dropped from the parameter vector. In the StMVAR model, M1=0. In the G-StMVAR model, the first M1 regimes are GMVAR type and the rest M2 regimes are StMVAR type. In StMVAR and G-StMVAR models, the degrees of freedom parameters in \nu should be strictly larger than two.

The notation is similar to the cited literature.


3D array containing the ((dp)x(dp)) "bold A" matrices related to each mixture component VAR-process, obtained from form_boldA. Will be computed if not given.


If NULL a reduced form model is considered. For structural model, should be a list containing the following elements:

  • W - a (dxd) matrix with its entries imposing constraints on W: NA indicating that the element is unconstrained, a positive value indicating strict positive sign constraint, a negative value indicating strict negative sign constraint, and zero indicating that the element is constrained to zero.

  • C_lambda - a (d(M-1) x r) constraint matrix that satisfies (\lambda_{2},..., \lambda_{M}) = C_{\lambda} \gamma where \gamma is the new (r x 1) parameter subject to which the model is estimated (similarly to AR parameter constraints). The entries of C_lambda must be either positive or zero. Ignore (or set to NULL) if the eigenvalues \lambda_{mi} should not be constrained.

See Virolainen (2022) for the conditions required to identify the shocks and for the B-matrix as well (it is W times a time-varying diagonal matrix with positive diagonal entries).


Returns FALSE if modulus of any eigenvalue is larger or equal to 1-tolerance.


If the model is constrained, remove the constraints first with the function reform_constrained_pars.


Returns TRUE if the model is stationary and FALSE if not. Based on the argument tolerance, is_stationary may return FALSE when the parameter vector is in the stationarity region, but very close to the boundary (this is used to ensure numerical stability in estimation of the model parameters).


No argument checks!


  • Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.

  • Virolainen S. 2022. Structural Gaussian mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks. Unpublished working paper, available as arXiv:2007.04713.

  • Virolainen S. 2022. Gaussian and Student's t mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks in the Euro area. Unpublished working paper, available as arXiv:2109.13648.

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saviviro/gmvarkit documentation built on April 18, 2023, 8:55 a.m.