sstvars: toolkit for reduced form and structural smooth transition vector autoregressive models
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 the estimation of linear impulse response functions, nonlinear generalized impulse response functions, and generalized forecast error variance 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. package
Useful links: