Estimate Gaussian Mixture Vector Autoregressive Model


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Documentation for package ‘gmvarkit’ version 1.4.1

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A C D E F G I L N P Q R S U V W

-- A --

add_data Add data to an object of class 'gmvar' defining a GMVAR model
all_pos_ints Check whether all arguments are positive integers
alt_gmvar Construct a GMVAR model based on results from an arbitrary estimation round of 'fitGMVAR'

-- C --

calc_gradient Calculate gradient or Hessian matrix
calc_hessian Calculate gradient or Hessian matrix
change_parametrization Change parametrization of a parameter vector
change_regime Change regime parameters *upsilon_{m}* = (phi_{m,0},*phi_{m}*,sigma_{m}) of the given parameter vector
check_constraints Check the constraint matrix has the correct form
check_data Check the data is in the correct form
check_gmvar Checks whether the given object has class attribute 'gmvar'
check_null_data Checks whether the given object contains data
check_parameters Check that the given parameter vector satisfies the model assumptions
check_pMd Check that p, M, and d are correctly set
check_same_means Check whether the parametrization is correct for usage of same means restrictions
cond_moments Compute conditional moments of a GMVAR model
cond_moment_plot Conditional mean or variance plot for a GMVAR model

-- D --

diagnostic_plot Quantile residual diagnostic plot for a GMVAR model
diag_Omegas Simultaneously diagonalize two covariance matrices
dlogmultinorm Calculate logarithms of multiple multivariate normal densities with varying mean and constant covariance matrix

-- E --

eurusd Euro area and U.S. long-term government bond yields and Euro-U.S. dollar exchange rate.

-- F --

fitGMVAR Two-phase maximum likelihood estimation of a GMVAR model
format_valuef Function factory for value formatting
form_boldA Form the ((dp)x(dp)) "bold A" matrices related to the VAR processes

-- G --

GAfit Genetic algorithm for preliminary estimation of a GMVAR model
get_alpha_mt Get mixing weights alpha_mt (this function is for internal use)
get_boldA_eigens Calculate absolute values of the eigenvalues of the "bold A" matrices containing the AR coefficients
get_foc Calculate gradient or Hessian matrix
get_gradient Calculate gradient or Hessian matrix
get_hessian Calculate gradient or Hessian matrix
get_IC Calculate AIC, HQIC, and BIC
get_minval Returns the default smallest allowed log-likelihood for given data.
get_omega_eigens Calculate the eigenvalues of the "Omega" error term covariance matrices
get_regime_autocovs Calculate regimewise autocovariance matrices
get_regime_autocovs_int Calculate regimewise autocovariance matrices
get_regime_means Calculate regime means mu_{m}
get_regime_means_int Calculate regime means mu_{m}
get_Sigmas Calculate the dp-dimensional covariance matrices Sigma_{m,p} in the mixing weights of the GMVAR model.
get_soc Calculate gradient or Hessian matrix
get_test_Omega Compute covariance matrix Omega used in quantile residual tests
get_unconstrained_structural_pars Get structural parameters that indicate there are no constraints
GIRF Estimate generalized impulse response function for a structural GMVAR model.
GMVAR Create a class 'gmvar' object defining a reduced form or structural GMVAR model
gmvarkit gmvarkit: Estimate Gaussian Mixture Vector Autoregressive (GMVAR) model
gmvar_to_sgmvar Switch from two-regime reduced form GMVAR model to a structural GMVAR model.

-- I --

in_paramspace Determine whether the parameter vector lies in the parameter space
in_paramspace_int Determine whether the parameter vector lies in the parameter space
is_stationary Check the stationary condition of a given GMVAR model
iterate_more Maximum likelihood estimation of a GMVAR model with preliminary estimates

-- L --

logLik.gmvar Create a class 'gmvar' object defining a reduced form or structural GMVAR model
loglikelihood Compute log-likelihood of a GMVAR model using parameter vector
loglikelihood_int Compute log-likelihood of a Gaussian mixture vector autoregressive model
LR_test Perform likelihood ratio test for a GMVAR or SGMVAR model

-- N --

n_params Calculate the number of parameters in GMVAR model parameter vector

-- P --

pick_allA Pick coefficient all matrices
pick_all_phi0_A Pick all phi_{m,0} or mu_{m} and A_{m,1},...,A_{m,p} parameter values
pick_alphas Pick mixing weight parameters alpha_{m}, m=1,...,M
pick_Am Pick coefficient matrices
pick_Ami Pick coefficient matrix
pick_lambdas Pick the structural parameters eigenvalue 'lambdas'
pick_Omegas Pick covariance matrices
pick_phi0 Pick phi_{m,0} or mu_{m}, m=1,..,M vectors
pick_regime Pick regime parameters *upsilon_{m}* = (phi_{m,0},*phi_{m}*,sigma_{m})
pick_W Pick the structural parameter matrix W
plot.girf Estimate generalized impulse response function for a structural GMVAR model.
plot.gmvar Create a class 'gmvar' object defining a reduced form or structural GMVAR model
plot.gmvarpred plot method for class 'gmvarpred' objects
plot.qrtest Quantile residual tests
predict.gmvar Predict method for class 'gmvar' objects
print.girf Estimate generalized impulse response function for a structural GMVAR model.
print.gmvar Create a class 'gmvar' object defining a reduced form or structural GMVAR model
print.gmvarpred Print method for class 'gmvarpred' objects
print.gmvarsum Summary print method from objects of class 'gmvarsum'
print.lr Perform likelihood ratio test for a GMVAR or SGMVAR model
print.qrtest Quantile residual tests
print.wald Perform Wald test for a GMVAR or SGMVAR model
print_std_errors Print standard errors of GMVAR model in the same form as the model estimates are printed
profile_logliks Plot profile log-likehoods around the estimates

-- Q --

quantile_residuals Calculate multivariate quantile residuals of a GMVAR model
quantile_residuals_int Calculate multivariate quantile residuals of GMVAR model
quantile_residual_tests Quantile residual tests

-- R --

random_coefmats Create random VAR-model (dxd) coefficient matrices A.
random_coefmats2 Create random stationary VAR model (dxd) coefficient matrices A.
random_covmat Create random VAR model error term covariance matrix
random_ind Create random mean-parametrized parameter vector of a GMVAR model that may not be stationary
random_ind2 Create somewhat random parameter vector of a GMVAR model that is always stationary
redecompose_Omegas In the decomposition of the covariance matrices (Muirhead, 1982, Theorem A9.9), change the order of the covariance matrices.
reform_constrained_pars Reform constrained parameter vector into the "standard" form
reform_data Reform data
reform_structural_pars Reform structural parameter vector into the "standard" form
regime_distance Calculate "distance" between two (scaled) regimes *upsilon_{m}* = (phi_{m,0},*phi_{m}*,sigma_{m})
reorder_W_columns Reorder columns of the W-matrix and lambda parameters of a structural GMVAR model.
residuals.gmvar Create a class 'gmvar' object defining a reduced form or structural GMVAR model

-- S --

simulateGMVAR Simulate from GMVAR process
smart_covmat Create random VAR-model (dxd) error term covariance matrix Omega fairly close to a given *positive definite* covariance matrix using (scaled) Wishart distribution
smart_ind Create random parameter vector of a GMVAR model fairly close to a given parameter vector
sort_and_standardize_alphas Sort mixing weight parameters in a decreasing order and standardize them to sum to one.
sort_components Sort components in parameter vector according to mixing weights into a decreasing order
sort_W_and_lambdas Sort the columns of W matrix by sorting the lambda parameters of the second regime to increasing order
standard_errors Calculate standard errors for estimates of GMVAR model
summary.gmvar Create a class 'gmvar' object defining a reduced form or structural GMVAR model
swap_parametrization Swap the parametrization of a GMVAR model
swap_W_signs Swap all signs in pointed columns a the W matrix of a structural GMVAR model.

-- U --

uncond_moments Calculate the unconditional mean, variance, the first p autocovariances, and the first p autocorrelations of a GMVAR process
uncond_moments_int Calculate the unconditional mean, variance, the first p autocovariances, and the first p autocorrelations of a GMVAR process
unvec Reverse vectorization operator
unvech Reverse operator of the parsimonious vectorization operator 'vech'
unWvec Reverse vectorization operator that restores zeros
update_numtols Update the stationarity and positive definiteness numerical tolerances of an existing class 'gmvar' model.

-- V --

VAR_pcovmat Calculate the dp-dimensional covariance matrix of p consecutive observations of a VAR process
vec Vectorization operator
vech Parsimonious vectorization operator for symmetric matrices

-- W --

Wald_test Perform Wald test for a GMVAR or SGMVAR model
Wvec Vectorization operator that removes zeros