Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models
Create random distribution parameter values
Create a special matrix J
Calculate the eigenvalues of the "Omega" error term covariance matrice...
Calculate the eigenvalues of the "Omega" error term covariance matrice...
Calculate regimewise autocovariance matrices
Calculate regime means
Calculate residuals of a smooth transition VAR
Pick coefficient matrices
Pick the structural parameter matrix W
Pick transition weight parameters
Reverse operator of the parsimonious vectorization operator vech
Get the new starting time of series that is forwarded some number of s...
Check whether all arguments are positive integers
Construct a STVAR model based on results from an arbitrary estimation ...
Calculate upper bound for the joint spectral radius of the "companion ...
Calculate upper bound for the joint spectral radius of a set of matric...
Create Matrix F_i
Calculate gradient or Hessian matrix
Change parametrization of a parameter vector
Change the parameters of a specific regime of the given parameter vect...
Check Matrix B Invertibility with C++ (Internal Function)
Check the constraint matrix has the correct form
Check the data is in the correct form
Checks whether the given exogenous transition weights for simulation a...
Check whether the parameter vector is in the parameter space and throw...
Check that p, M, and d are correctly set
Checks whether the given object has class attribute 'stvar'
Check the argument weightfun_pars
Simultaneously diagonalize two covariance matrices
Residual diagnostic plot for a STVAR model
Internal estimation function for estimating STVAR model when bootstrap...
Maximum likelihood estimation of a structural STVAR model based on pre...
Two-phase maximum likelihood estimation of a reduced form smooth trans...
Form the "bold A" matrices related to the VAR processes
Function factory for value formatting
Genetic algorithm for preliminary estimation of a STVAR models
Calculate log multivariate Gaussian densities
Calculate the conditional means of the process
Calculate log multivariate Gaussian densities
Get the transition weights alpha_mt
Calculate absolute values of the eigenvalues of the "bold A" matrices ...
Calculate absolute values of the eigenvalues of the "bold A" matrices ...
Calculate the impact matrix for all for models with a non-Ga...
Switch from two-regime reduced form STVAR model to a structural model ...
Calculate AIC, HQIC, and BIC
Returns the default smallest allowed log-likelihood for given data.
Reverse vectorization operator that restores zeros
Calculate the dp-dimensional covariance matrices in the...
Calculate symmetric square root matrix of a positive definite covarian...
Estimate generalized forecast error variance decomposition for structu...
Estimate generalized impulse response function for structural STVAR mo...
Determine whether the parameter vector is in the parameter space
Calculate log independent multivariate Student's t densities
Maximum likelihood estimation of a reduced form or structural STVAR mo...
Estimate linear impulse response function based on a single regime of ...
Log-likelihood function
Perform likelihood ratio test for a STVAR model
Compute the j:th power of a square matrix A
Calculate the number of (freely estimaed) parameters in the model
Reorder columns of a square matrix so that the first nonzero elements ...
Pick all coefficient matrices
Pick coefficient matrix
Pick distribution parameters
Pick the structural parameter eigenvalues 'lambdas'
Pick covariance matrices
Pick or , m=1,..,M vectors
Pick regime parameters
Perform adjusted Portmanteau test for a STVAR model
Predict method for class 'stvar' objects
Print method for the class hypotest
Summary print method from objects of class 'stvarsum'
Plot profile log-likelihood functions about the estimates
Create random VAR model coefficient matrices .
Create random stationary VAR model coefficient matrices .
Create random VAR model error term covariance matrix
Create random VAR model impact matrix
Create random mean parametrized parameter vector
Create random transition weight parameter values
Perform Rao's score test for a STVAR model
In the decomposition of the covariance matrices (Muirhead, 1982, Theor...
Reform constrained parameter vector into the "standard" form
Reform data
Calculate "distance" between two (scaled) regimes upsilon_{m} $ = (\ph...
Reorder columns of impact matrix B (and lambda parameters if any) of a...
Simulate method for class 'stvar' objects
Simulate observations from a regime of a STVAR model
Create random VAR model error term covariance matrix f...
Create random distribution parameter values close to given values
Create a random VAR model error impact matrix fairly close ...
Create random parameter vector that is fairly close to a given paramet...
Create random transition weight parameter values
Sort and sign change the columns of the impact matrices of the regimes...
Sort regimes in parameter vector according to transition weights into ...
sstvars: toolkit for reduced form and structural smooth transition vec...
Check the stability condition for each of the regimes
Calculate standard errors for estimates of a smooth transition VAR mod...
Calculate log multivariate Student's t densities
Create a class 'stvar' object defining a reduced form or structural sm...
Swap all signs in pointed columns of the impact matrix of a structural...
Swap the parametrization of a STVAR model
Calculate the unconditional means, variances, the first p autocovarian...
Reverse vectorization operator
Calculate the dp-dimensional covariance matrix of p consecutive observ...
Vectorization operator
Parsimonious vectorization operator for symmetric matrices
Perform Wald test for a STVAR model
Warn about near-unit-roots in some regimes
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>.