Package: gmvarkit 2.1.3

Savi Virolainen

gmvarkit: Estimate Gaussian and Student's t Mixture Vector Autoregressive Models

Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (forthcoming) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen (2022) <doi:10.48550/arXiv.2109.13648>.

Authors:Savi Virolainen [aut, cre]

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# Install 'gmvarkit' in R:
install.packages('gmvarkit', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/saviviro/gmvarkit/issues

Datasets:
  • euromone - A monthly Euro area data covering the period from January 1999 to December 2021 (276 observations) and consisting four variables: cyclical component of log industrial production index, the log-difference of harmonized consumer price index, the log-difference of Brent crude oil prices (Europe), and an interest rate variable. The interest rate variable is the Euro overnight index average rate (EONIA) from January 1999 to October 2008, and after that the Wu and Xia (2016) shadow rate, which is not constrained by the zero lower bound and also quantifies unconventional monetary policy measures. The log-difference of the harmonized consumer price index is multiplied by hundred and the log-difference of oil price by ten. This data is the one that was used in Virolainen (2022).
  • gdpdef - U.S. real GDP percent change and GDP implicit price deflator percent change.
  • usamon - A quarterly U.S. data covering the period from 1954Q3 to 2021Q4 (270 observations) and consisting four variables: the log-difference of real GDP, the log-difference of GDP implicit price deflator, the log-difference of producer price index (all commodities), and an interest rate variable. The interest rate variable is the effective federal funds rate from 1954Q3 to 2008Q2 and after that the Wu and Xia (2016) shadow rate, which is not constrained by the zero lower bound and also quantifies unconventional monetary policy measures. The log-differences of the GDP, GDP deflator, and producer price index are multiplied by hundred. This data is used in Virolainen (forthcoming).
  • usamone - A quarterly U.S. data covering the period from 1954Q3 to 2021Q4 (270 observations) and consisting four variables: cyclical component of the log of real GDP, the log-difference of GDP implicit price deflator, the log-difference of producer price index (all commodities), and an interest rate variable. The interest rate variable is the effective federal funds rate from 1954Q3 to 2008Q2 and after that the Wu and Xia (2016) shadow rate, which is not constrained by the zero lower bound and also quantifies unconventional monetary policy measures. The log-differences of the GDP deflator and producer price index are multiplied by hundred.

3.81 score 1 stars 43 scripts 955 downloads 49 exports 9 dependencies

Last updated 22 days agofrom:1e4565b4fb. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 04 2024
R-4.5-linuxOKDec 04 2024

Exports:add_dataalt_gmvaralt_gsmvarcalc_gradientcalc_hessiancheck_parameterscond_moment_plotcond_momentsdiag_Omegasdiagnostic_plotestimate_sgsmvarfitGMVARfitGSMVARGAfitget_boldA_eigensget_focget_gradientget_hessianget_omega_eigensget_regime_autocovsget_regime_meansget_socGFEVDGIRFGMVARgmvar_to_gsmvargmvar_to_sgmvarGSMVARgsmvar_to_sgsmvarin_paramspaceiterate_morelinear_IRFloglikelihoodLR_testPearson_residualsprint_std_errorsprofile_logliksquantile_residual_testsquantile_residualsRao_testredecompose_Omegasreorder_W_columnssimulateGMVARstmvar_to_gstmvarswap_parametrizationswap_W_signsuncond_momentsupdate_numtolsWald_test

Dependencies:BHBrobdingnaggsllatticeMatrixmvnfastpbapplyRcppRcppArmadillo

gmvarkit: A Family of Mixture Vector Autoregressive Models in R

Rendered fromgmvarkit-vignette.Rnwusingutils::Sweaveon Dec 04 2024.

Last update: 2024-12-04
Started: 2021-11-22

Readme and manuals

Help Manual

Help pageTopics
gmvarkit: Estimate Gaussian and Student's t Mixture Vector Autoregressive Modelsgmvarkit-package gmvarkit
Add data to an object of class 'gsmvar' defining a GMVAR, StMVAR, or G-StMVAR modeladd_data
DEPRECATED! USE THE FUNCTION alt_gsmvar INSTEAD! Construct a GMVAR model based on results from an arbitrary estimation round of 'fitGSMVAR'alt_gmvar
Construct a GMVAR, StMVAR, or G-StMVAR model based on results from an arbitrary estimation round of 'fitGSMVAR'alt_gsmvar
Calculate gradient or Hessian matrixcalc_gradient calc_hessian get_foc get_gradient get_hessian get_soc
Check that the given parameter vector satisfies the model assumptionscheck_parameters
Conditional mean or variance plot for a GMVAR, StMVAR, or G-StMVAR modelcond_moment_plot
Compute conditional moments of a GMVAR, StMVAR, or G-StMVAR modelcond_moments
Simultaneously diagonalize two covariance matricesdiag_Omegas
Quantile residual diagnostic plot for a GMVAR, StMVAR, or G-StMVAR modeldiagnostic_plot
Maximum likelihood estimation of a structural GMVAR, StMVAR, or G-StMVAR model with preliminary estimatesestimate_sgsmvar
A monthly Euro area data covering the period from January 1999 to December 2021 (276 observations) and consisting four variables: cyclical component of log industrial production index, the log-difference of harmonized consumer price index, the log-difference of Brent crude oil prices (Europe), and an interest rate variable. The interest rate variable is the Euro overnight index average rate (EONIA) from January 1999 to October 2008, and after that the Wu and Xia (2016) shadow rate, which is not constrained by the zero lower bound and also quantifies unconventional monetary policy measures. The log-difference of the harmonized consumer price index is multiplied by hundred and the log-difference of oil price by ten. This data is the one that was used in Virolainen (2022).euromone
DEPRECATED! USE THE FUNCTION fitGSMVAR INSTEAD! Two-phase maximum likelihood estimation of a GMVAR modelfitGMVAR
Two-phase maximum likelihood estimation of a GMVAR, StMVAR, or G-StMVAR modelfitGSMVAR
Genetic algorithm for preliminary estimation of a GMVAR, StMVAR, or G-StMVAR modelGAfit
U.S. real GDP percent change and GDP implicit price deflator percent change.gdpdef
Calculate absolute values of the eigenvalues of the "bold A" matrices containing the AR coefficientsget_boldA_eigens
Calculate the eigenvalues of the "Omega" error term covariance matricesget_omega_eigens
Calculate regimewise autocovariance matricesget_regime_autocovs
Calculate regime means mu_{m}get_regime_means
Estimate generalized forecast error variance decomposition for structural (and reduced form) GMVAR, StMVAR, and G-StMVAR models.GFEVD plot.gfevd print.gfevd
Estimate generalized impulse response function for structural (and reduced form) GMVAR, StMVAR, and G-StMVAR models.GIRF plot.girf print.girf
DEPRECATED! USE THE FUNCTION GSMVAR INSTEAD! Create a class 'gsmvar' object defining a reduced form or structural GMVAR modelGMVAR
Makes class 'gmvar' objects compatible with the functions using class 'gsmvar' objectsgmvar_to_gsmvar
DEPRECATED! USE THE FUNCTION fitGSMVAR INSTEAD! Switch from two-regime reduced form GMVAR model to a structural model.gmvar_to_sgmvar
Create a class 'gsmvar' object defining a reduced form or structural GMVAR, StMVAR, or G-StMVAR modelGSMVAR logLik.gsmvar plot.gsmvar print.gsmvar residuals.gsmvar summary.gsmvar
Switch from two-regime reduced form GMVAR, StMVAR, or G-StMVAR model to a structural model.gsmvar_to_sgsmvar
Determine whether the parameter vector lies in the parameter spacein_paramspace
Determine whether the parameter vector lies in the parameter spacein_paramspace_int
Maximum likelihood estimation of a GMVAR, StMVAR, or G-StMVAR model with preliminary estimatesiterate_more
Estimate linear impulse response function based on a single regime of a structural GMVAR, StMVAR, or G-StMVAR model.linear_IRF plot.irf print.irf
Compute log-likelihood of a GMVAR, StMVAR, or G-StMVAR model using parameter vectorloglikelihood
Perform likelihood ratio test for a GMVAR, StMVAR, or G-StMVAR modelLR_test
Calculate multivariate Pearson residuals of a GMVAR, StMVAR, or G-StMVAR modelPearson_residuals
plot method for class 'gmvarpred' objectsplot.gmvarpred print.gmvarpred
plot method for class 'gsmvarpred' objectsplot.gsmvarpred
Quantile residual testsplot.qrtest print.qrtest quantile_residual_tests
DEPRECATED! USE THE FUNCTION predict.gsmvar INSTEAD! Predict method for class 'gmvar' objectspredict.gmvar
Predict method for class 'gsmvar' objectspredict.gsmvar
Print standard errors of a GMVAR, StMVAR, or G-StMVAR model in the same form as the model estimates are printedprint_std_errors
Deprecated S3 methods for the deprecated class 'gmvar'logLik.gmvar plot.gmvar print.gmvar residuals.gmvar summary.gmvar
Summary print method from objects of class 'gmvarsum'print.gmvarsum
Print method for class 'gsmvarpred' objectsprint.gsmvarpred
Summary print method from objects of class 'gsmvarsum'print.gsmvarsum
Print method for the class hypotestprint.hypotest
Plot profile log-likehoods around the estimatesprofile_logliks
Calculate multivariate quantile residuals of a GMVAR, StMVAR, or G-StMVAR modelquantile_residuals
Create somewhat random parameter vector of a GMVAR, StMVAR, or G-StMVAR model that is always stationaryrandom_ind2
Perform Rao's score test for a GSMVAR modelRao_test
In the decomposition of the covariance matrices (Muirhead, 1982, Theorem A9.9), change the order of the covariance matrices.redecompose_Omegas
Reorder columns of the W-matrix and lambda parameters of a structural GMVAR, StMVAR, or G-StMVAR model.reorder_W_columns
Simulate method for class 'gsmvar' objectssimulate.gsmvar
DEPRECATED! USE THE FUNCTION simulate.gsmvar INSTEAD! Simulate from GMVAR processsimulateGMVAR
Estimate a G-StMVAR model based on a StMVAR model that has large degrees of freedom parametersstmvar_to_gstmvar
Swap the parametrization of a GMVAR, StMVAR, or G-StMVAR modelswap_parametrization
Swap all signs in pointed columns a the W matrix of a structural GMVAR, StMVAR, or G-StMVAR model.swap_W_signs
Calculate the unconditional mean, variance, the first p autocovariances, and the first p autocorrelations of a GMVAR, StMVAR, or G-StMVAR processuncond_moments
Update the stationarity and positive definiteness numerical tolerances of an existing class 'gsmvar' model.update_numtols
A quarterly U.S. data covering the period from 1954Q3 to 2021Q4 (270 observations) and consisting four variables: the log-difference of real GDP, the log-difference of GDP implicit price deflator, the log-difference of producer price index (all commodities), and an interest rate variable. The interest rate variable is the effective federal funds rate from 1954Q3 to 2008Q2 and after that the Wu and Xia (2016) shadow rate, which is not constrained by the zero lower bound and also quantifies unconventional monetary policy measures. The log-differences of the GDP, GDP deflator, and producer price index are multiplied by hundred. This data is used in Virolainen (forthcoming).usamon
A quarterly U.S. data covering the period from 1954Q3 to 2021Q4 (270 observations) and consisting four variables: cyclical component of the log of real GDP, the log-difference of GDP implicit price deflator, the log-difference of producer price index (all commodities), and an interest rate variable. The interest rate variable is the effective federal funds rate from 1954Q3 to 2008Q2 and after that the Wu and Xia (2016) shadow rate, which is not constrained by the zero lower bound and also quantifies unconventional monetary policy measures. The log-differences of the GDP deflator and producer price index are multiplied by hundred.usamone
Perform Wald test for a GMVAR, StMVAR, or G-StMVAR modelWald_test