check_constraints: Check the constraint matrix has the correct form

Description Usage Arguments Details Value

View source: R/argumentChecks.R

Description

check_constraints checks that the constraints are correctly set.

Usage

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check_constraints(
  p,
  M,
  d,
  constraints = NULL,
  same_means = NULL,
  structural_pars = NULL
)

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.

constraints

a size (Mpd^2 x q) constraint matrix C specifying general linear constraints to the autoregressive parameters. We consider constraints of form (φ_{1},...,φ_{M}) = C ψ, where φ_{m} = (vec(A_{m,1}),...,vec(A_{m,p}) (pd^2 x 1), m=1,...,M, contains the coefficient matrices and ψ (q x 1) contains the related parameters. For example, to restrict the AR-parameters to be the same for all regimes, set C= [I:...:I]' (Mpd^2 x pd^2) where I = diag(p*d^2). Ignore (or set to NULL) if linear constraints should not be employed.

same_means

Restrict the mean parameters of some regimes to be the same? Provide a list of numeric vectors such that each numeric vector contains the regimes that should share the common mean parameters. For instance, if M=3, the argument list(1, 2:3) restricts the mean parameters of the second and third regime to be the same but the first regime has freely estimated (unconditional) mean. Ignore or set to NULL if mean parameters should not be restricted to be the same among any regimes. This constraint is available only for mean parametrized models; that is, when parametrization="mean".

structural_pars

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 (λ_{2},..., λ_{M}) = C_{λ} γ where γ 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 λ_{mi} should not be constrained.

See Virolainen (2020) 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).

Details

If is.null(constraints), then this function doesn't do anything.

Value

Checks the constraint matrix C and throws an error if something is wrong.


saviviro/gmvarkit documentation built on Oct. 25, 2021, 2:14 a.m.