sstvars1.0.1 package

Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

random_distpars

Create random distribution parameter values

create_J_matrix

Create a special matrix J

get_omega_eigens

Calculate the eigenvalues of the "Omega" error term covariance matrice...

get_omega_eigens_par

Calculate the eigenvalues of the "Omega" error term covariance matrice...

get_regime_autocovs

Calculate regimewise autocovariance matrices

get_regime_means

Calculate regime means μm\mu_{m}

get_residuals

Calculate residuals of a smooth transition VAR

pick_Am

Pick coefficient matrices

pick_W

Pick the structural parameter matrix W

pick_weightpars

Pick transition weight parameters

unvech

Reverse operator of the parsimonious vectorization operator vech

get_new_start

Get the new starting time of series that is forwarded some number of s...

all_pos_ints

Check whether all arguments are positive integers

alt_stvar

Construct a STVAR model based on results from an arbitrary estimation ...

bound_JSR

Calculate upper bound for the joint spectral radius of the "companion ...

bound_jsr_G

Calculate upper bound for the joint spectral radius of a set of matric...

create_Fi_matrix

Create Matrix F_i

calc_gradient

Calculate gradient or Hessian matrix

change_parametrization

Change parametrization of a parameter vector

change_regime

Change the parameters of a specific regime of the given parameter vect...

check_Bt_Cpp

Check Matrix B Invertibility with C++ (Internal Function)

check_constraints

Check the constraint matrix has the correct form

check_data

Check the data is in the correct form

check_exoweights

Checks whether the given exogenous transition weights for simulation a...

check_params

Check whether the parameter vector is in the parameter space and throw...

check_pMd

Check that p, M, and d are correctly set

check_stvar

Checks whether the given object has class attribute 'stvar'

check_weightfun_pars

Check the argument weightfun_pars

diag_Omegas

Simultaneously diagonalize two covariance matrices

diagnostic_plot

Residual diagnostic plot for a STVAR model

fitbsSSTVAR

Internal estimation function for estimating STVAR model when bootstrap...

fitSSTVAR

Maximum likelihood estimation of a structural STVAR model based on pre...

fitSTVAR

Two-phase maximum likelihood estimation of a reduced form smooth trans...

form_boldA

Form the ((dp)x(dp))((dp)x(dp)) "bold A" matrices related to the VAR processes

format_valuef

Function factory for value formatting

GAfit

Genetic algorithm for preliminary estimation of a STVAR models

Gaussian_densities_const_Cpp

Calculate log multivariate Gaussian densities

get_mu_yt_Cpp

Calculate the conditional means of the process

Gaussian_densities_Cpp

Calculate log multivariate Gaussian densities

get_alpha_mt

Get the transition weights alpha_mt

get_boldA_eigens

Calculate absolute values of the eigenvalues of the "bold A" matrices ...

get_boldA_eigens_par

Calculate absolute values of the eigenvalues of the "bold A" matrices ...

get_Bt_Cpp

Calculate the impact matrix BtB_t for all tt for models with a non-Ga...

get_hetsked_sstvar

Switch from two-regime reduced form STVAR model to a structural model ...

get_IC

Calculate AIC, HQIC, and BIC

get_minval

Returns the default smallest allowed log-likelihood for given data.

unWvec

Reverse vectorization operator that restores zeros

get_Sigmas

Calculate the dp-dimensional covariance matrices Σm,p\Sigma_{m,p} in the...

get_symmetric_sqrt

Calculate symmetric square root matrix of a positive definite covarian...

GFEVD

Estimate generalized forecast error variance decomposition for structu...

GIRF

Estimate generalized impulse response function for structural STVAR mo...

in_paramspace

Determine whether the parameter vector is in the parameter space

ind_Student_densities_Cpp

Calculate log independent multivariate Student's t densities

iterate_more

Maximum likelihood estimation of a reduced form or structural STVAR mo...

linear_IRF

Estimate linear impulse response function based on a single regime of ...

loglikelihood

Log-likelihood function

LR_test

Perform likelihood ratio test for a STVAR model

mat_power

Compute the j:th power of a square matrix A

n_params

Calculate the number of (freely estimaed) parameters in the model

order_B

Reorder columns of a square matrix so that the first nonzero elements ...

pick_allA

Pick all coefficient matrices

pick_Ami

Pick coefficient matrix

pick_distpars

Pick distribution parameters

pick_lambdas

Pick the structural parameter eigenvalues 'lambdas'

pick_Omegas

Pick covariance matrices

pick_phi0

Pick ϕm,0\phi_{m,0} or μm\mu_{m}, m=1,..,M vectors

pick_regime

Pick regime parameters

Portmanteau_test

Perform adjusted Portmanteau test for a STVAR model

predict.stvar

Predict method for class 'stvar' objects

print.hypotest

Print method for the class hypotest

print.stvarsum

Summary print method from objects of class 'stvarsum'

profile_logliks

Plot profile log-likelihood functions about the estimates

random_coefmats

Create random VAR model (dxd)(dxd) coefficient matrices AA.

random_coefmats2

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

random_covmat

Create random VAR model error term covariance matrix

random_impactmat

Create random VAR model impact matrix

random_ind

Create random mean parametrized parameter vector

random_weightpars

Create random transition weight parameter values

Rao_test

Perform Rao's score test for a STVAR model

redecompose_Omegas

In the decomposition of the covariance matrices (Muirhead, 1982, Theor...

reform_constrained_pars

Reform constrained parameter vector into the "standard" form

reform_data

Reform data

regime_distance

Calculate "distance" between two (scaled) regimes upsilon_{m} $ = (\ph...

reorder_B_columns

Reorder columns of impact matrix B (and lambda parameters if any) of a...

simulate.stvar

Simulate method for class 'stvar' objects

simulate_from_regime

Simulate observations from a regime of a STVAR model

smart_covmat

Create random VAR model (dxd)(dxd) error term covariance matrix Ω\Omegaf...

smart_distpars

Create random distribution parameter values close to given values

smart_impactmat

Create a random VAR model (dxd)(dxd) error impact matrix BBfairly close ...

smart_ind

Create random parameter vector that is fairly close to a given paramet...

smart_weightpars

Create random transition weight parameter values

sort_impactmats

Sort and sign change the columns of the impact matrices of the regimes...

sort_regimes

Sort regimes in parameter vector according to transition weights into ...

sstvars-package

sstvars: toolkit for reduced form and structural smooth transition vec...

stab_conds_satisfied

Check the stability condition for each of the regimes

standard_errors

Calculate standard errors for estimates of a smooth transition VAR mod...

Student_densities_Cpp

Calculate log multivariate Student's t densities

STVAR

Create a class 'stvar' object defining a reduced form or structural sm...

swap_B_signs

Swap all signs in pointed columns of the impact matrix of a structural...

swap_parametrization

Swap the parametrization of a STVAR model

uncond_moments

Calculate the unconditional means, variances, the first p autocovarian...

unvec

Reverse vectorization operator

VAR_pcovmat

Calculate the dp-dimensional covariance matrix of p consecutive observ...

vec

Vectorization operator

vech

Parsimonious vectorization operator for symmetric matrices

Wald_test

Perform Wald test for a STVAR model

warn_eigens

Warn about near-unit-roots in some regimes

Wvec

Vectorization operator that removes zeros

Maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, and calculation of impulse response functions, generalized impulse response functions, and generalized forecast error variance decompositions. See Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2024) <doi:10.48550/arXiv.2403.14216>, Savi Virolainen (2024) <doi:10.48550/arXiv.2404.19707>.

  • Maintainer: Savi Virolainen
  • License: GPL-3
  • Last published: 2024-05-29