is_stationary: Check the stationary condition of a given GMVAR, StMVAR, or...
In gmvarkit: Estimate Gaussian and Student's t Mixture Vector Autoregressive Models

is_stationary

R Documentation

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

Description

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

Usage

is_stationary(
  p,
  M,
  d,
  params,
  all_boldA = NULL,
  structural_pars = NULL,
  tolerance = 0.001
)

Arguments

`p`	a positive integer specifying the autoregressive order of the model.
`M`	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`.
`d`	the number of time series in the system.
`params`	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 `m`th mixture component. Above, `\phi_{m,0}` is the intercept parameter, `A_{m,i}` denotes the `i`th coefficient matrix of the `m`th 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.
`all_boldA`	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.
`structural_pars`	If `NULL` a reduced form model is considered. Reduced models can be used directly as recursively identified structural models. For a structural model identified by conditional heteroskedasticity, should be a list containing at least the first one of 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. `fixed_lambdas` - a length `d(M-1)` numeric vector (`\lambda``_{2},...,` `\lambda``_{M})` with elements strictly larger than zero specifying the fixed parameter values for the parameters `\lambda_{mi}` should be constrained to. This constraint is alternative `C_lambda`. 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).
`tolerance`	Returns `FALSE` if modulus of any eigenvalue is larger or equal to `1-tolerance`.

Details

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

Value

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).

Warning

No argument checks!

References

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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gmvarkit documentation built on Nov. 15, 2023, 1:07 a.m.