Learn R Programming

⚠️There's a newer version (2.2.1) of this package.Take me there.

gmvarkit

The goal of gmvarkit is to provide tools to work with Gaussian Mixture Vector Autoregressive (GMVAR) model. Most importantly gmvarkit provides function fitGMVAR for two phase maximum likelihood estimation, but it also constains functions for quantile residual tests, graphical diagostics, forecasting and simulations. Also applying general linear constraints to the autoregressive parameters is supported.

Simple example

This is a basic example how to estimate a GMVAR model to an example data. The estimation process is computationally heavy and uses parallel computing.

# These examples use the data 'eurusd' which comes with the package, but in a scaled form.
data <- cbind(10*eurusd[,1], 100*eurusd[,2])
colnames(data) <- colnames(eurusd)

# GMVAR(2,2) model
fit22 <- fitGMVAR(data, p=2, M=2)
fit22

# GMVAR(2,2) model with autoregressive parameters restricted to be the same for all regimes
C_mat <- rbind(diag(2*2^2), diag(2*2^2))
fit22c <- fitGMVAR(data, p=2, M=2, constraints=C_mat)
fit22c

References

Kalliovirta L., Meitz M. and Saikkonen P. (2016) Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Kalliovirta L. and Saikkonen P. (2010) Reliable Residuals for Multivariate Nonlinear Time Series Models. Unpublished Revision of HECER Discussion Paper No. 247.

Copy Link

Version

Install

install.packages('gmvarkit')

Monthly Downloads

538

Version

1.0.3

License

GPL-3

Maintainer

Savi Virolainen

Last Published

September 22nd, 2018

Functions in gmvarkit (1.0.3)

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

Check that p, M and d are correctly set

get_regime_means_int

Calculate and return regime means \(\mu_{m}\)

get_regime_means

Calculate and return regime means \(\mu_{m}\)

sort_components

Sort components in parameter vector by mixing weights into a decreasing order

Create somewhat random parameter vector of GMVAR model fairly close to a given parameter vector

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

Pick \(\phi_{m,0}\) or \(\mu_{m}\), m=1,..,M vectors from the given parameter vector

Pick regime parameters \(\upsilon_{m}\)\( = (\phi_{m,0},\)\(\phi_{m}\)\(,\sigma_{m})\) from the given parameter vector.

Two-phase maximum likelihood estimation of GMVAR model

Function factory for value formatting

in_paramspace_int

Determine whether the parameter vector lies in the parameter space or not

diagnostic_plot

Quantile residual diagnostic plot for GMVAR model

Compute covariance matrix Omega used in quantile residual tests

Calculate logarithms of multiple multivariate normal densities with varying mean and constant covariance matrix

Maximum likelihood estimation of GMVAR model with preliminary estimates

Check the stationary condition of given GMVAR model

Reverse vectorization operator.

Compute log-likelihood of GMVAR model using parameter vector

check_parameters

Check that the given parameter vector satisfies model assumptions

Pick covariance matrices

Pick coefficient all matrices

Reverse operator of the parsimonious vectorization operator vech().

random_coefmats2

Create somewhat random stationary VAR model \((dxd)\) coefficient matrices \(A\).

random_coefmats

Create random VAR-model \((dxd)\) coefficient matrices \(A\).

Pick coefficient matrices

Pick coefficient matrix

standard_errors

Calculate standard errors for estimates of GMVAR model

Calculate AIC, HQIC and BIC

Add data to object of class 'gmvar' defining a GMVAR model

get_boldA_eigens

Calculate absolute values of the eigenvalues of the "bold A" matrices containing the AR coefficients

swap_parametrization

Swap the parametrization of object of class 'gmvar' defining a GMVAR model

loglikelihood_int

Compute the log-likelihood of Gaussian Mixture Vector Autoregressive model

Calculate the number of parameters in GMVAR model parameter vector

print.gmvarpred

print method for class 'gmvarpred' objects

print_std_errors

Print standard errors of GMVAR model in the same form as the model estimates are printed

Checks whether the given object has class attribute "gmvar"

Summary print method from objects of class 'gmvarsum'

Determine whether the parameter vector lies in the parameter space or not

Simulate from GMVAR process

check_null_data

Checks whether the given object contains data or not

pick_all_phi0_A

Pick all \(\phi_{m,0}\) or \(\mu_{m}\) and \(A_{m}\) parameter values from the given parameter vector.

plot method for class 'gmvarpred' objects

Predict method for class 'gmvar' objects

Pick mixing weight parameters \(\alpha_{m}, m=1,...,M\) from the given parameter vector.

Quantile residual tests

Create somewhat random parameter vector of GMVAR model that is always stationary

regime_distance

Calculate "distance" between two (scaled) regimes \(\upsilon_{m}\)\( = (\phi_{m,0},\)\(\phi_{m}\)\(,\sigma_{m})\)

quantile_residuals

Calculate multivariate quantile residuals of GMVAR model

Create somewhat random VAR-model \((dxd)\) error term covariance matrix \(\Omega\) fairly close to a given positive definite covariance matrix using (scaled) Wishart distribution.

quantile_residuals_int

Calculate multivariate quantile residuals of GMVAR model

reform_constrained_pars

Reform constrained parameter vector into the "standard" form

Vectorization operator.

Create somewhat random VAR model error term covariance matrix

Parsimonious vectorization operator for symmetric matrices.

Create somewhat random mean-parametrized parameter vector of GMVAR model, that may not be stationary!

Genetic algorithm for preliminary estimation of GMVAR model

change_parametrization

Change parametrization of the parameter vector

Create object of class 'gmvar' defining a GMVAR model

check_constraints

Check the constraint matrix has the correct form

Change regime parameters \(\upsilon_{m}\)\( = (\phi_{m,0},\)\(\phi_{m}\)\(,\sigma_{m})\) of the given parameter vector.

Check the data is in the correct form

gmvarkit: Estimate Gaussian Mixture Vector Autoregressive (GMVAR) model

Check whether all arguments are positive scalar whole numbers

Calculate gradient or Hessian matrix