sstvars is a package for reduced form and structural smooth transition vector
autoregressive models. The package implements various transition weight functions, conditional distributions,
identification methods, and parameter restrictions. The model parameters are estimated with the method of maximum
likelihood or penalized maximum likelihood by running multiple rounds of either a two-phase estimation procedure
or a three-phase procedure. In the former, a genetic algorithm is used to find starting values for a gradient based
variable metric algorithm. In the latter, nonlinear least squares (NLS) first used obtain initial estimates for some
of the parameters, then a genetic algorithm is used to find starting values for the rest of the parameters conditional
on the NLS estimates, and finally a gradient based variable metric algorithm is initialized from the estimates obtained
from the previous two steps. For evaluating the adequacy of the estimated models, sstvars utilizes residuals based
diagnostics and provides functions for graphical diagnostics as well as for calculating formal diagnostic tests.
sstvars also accommodates tools for conducting counterfactual analysis as well as computation of impulse
response functions, generalized impulse response functions, generalized forecast error variance decompositions,
and historical decompositions. Further functionality includes hypothesis testing, plotting the profile log-likelihood
functions about the estimate, simulation from STVAR processes, and forecasting, for example.
The vignette is a good place to start, and see also the readme file.
Maintainer: Savi Virolainen savi.virolainen@helsinki.fi (ORCID)
Useful links: