Estimate Gaussian or Student's t Mixture Vector Autoregressive
Model
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 (2020) <arXiv:2007.04713>,
Savi Virolainen (2021) <arXiv:2109.13648>.

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 (2021) <arXiv:2007.04713>,
Savi Virolainen (2021) <arXiv:2109.13648>.

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 (2022) <arXiv:2007.04713>,
Savi Virolainen (2022) <arXiv:2109.13648>.

c(
person("James", "Balamuta", , "james.balamuta@gmail.com", c("aut", "cph")),
person("Stephane", "Guerrier", , "stephane@illinois.edu", c("ctb","cre","cph")),
person("Roberto", "Molinari", , "roberto.molinari@unige.ch", c("ctb", "cph")),
person("Wenchao", "Yang", , "wyang40@illinois.edu", c("ctb"))
)
Generalized Method of Wavelet Moments (GMWM) is an estimation technique for the parameters of time series models. It uses the wavelet variance in a moment matching approach that makes it particularly suitable for the estimation of certain state-space models. Furthermore, there exists a robust implementation of GMWM, which allows the robust estimation of some state-space models and ARIMA models. Lastly, the package provides the ability to quickly generate time series data, perform different wavelet decompositions, and visualizations.
Rcpp (>= 0.11.3), scales, grid, reshape2, gridExtra, devtools,
utils, stats, methods, graphics, grDevices
James Balamuta [aut, cph],
Stephane Guerrier [ctb, cre, cph],
Roberto Molinari [ctb, cph],
Wenchao Yang [ctb]