# Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

## Documentation for package ‘sstvars’ version 1.1.5

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

sstvars-package | sstvars: toolkit for reduced form and structural smooth transition vector autoregressive models

acidata

A monthly U.S. data covering the period from 1961I to 2022III (735 observations) and consisting four variables. First, The Actuaries Climate Index (ACI), which is a measure of the frequency of severe weather and the extend changes in sea levels. Second, the monthly GDP growth rate constructed by the Federal Reserve Bank of Chicago from a collapsed dynamic factor analysis of a panel of 500 monthly measures of real economic activity and quarterly real GDP growth. Third, the monthly growth rate of the consumer price index (CPI). Third, an interest rate variable, which is the effective federal funds rate that is replaced by the the Wu and Xia (2016) shadow rate during zero-lower-bound periods. The Wu and Xia (2016) shadow rate is not bounded by the zero lower bound and also quantifies unconventional monetary policy measures, while it closely follows the federal funds rate when the zero lower bound does not bind.

alt_stvar | Construct a STVAR model based on results from an arbitrary estimation round of 'fitSTVAR'
bound_JSR | Calculate upper bound for the joint spectral radius of the "companion form AR matrices" of the regimes
bound_jsr_G | Calculate upper bound for the joint spectral radius of a set of matrices
calc_gradient | Calculate gradient or Hessian matrix
calc_hessian | Calculate gradient or Hessian matrix
check_params | Check whether the parameter vector is in the parameter space and throw error if not
diagnostic_plot | Residual diagnostic plot for a STVAR model
diag_Omegas | Simultaneously diagonalize two covariance matrices
filter_estimates | Filter inappropriate the estimates produced by fitSTVAR
fitSSTVAR | Maximum likelihood estimation of a structural STVAR model based on preliminary estimates from a reduced form model.
fitSTVAR | Two-phase or three-phase (penalized) maximum likelihood estimation of a reduced form smooth transition VAR model
GAfit | Genetic algorithm for preliminary estimation of reduced form STVAR models
gdpdef | U.S. real GDP percent change and GDP implicit price deflator percent change.
get_foc | Calculate gradient or Hessian matrix
get_gradient | Calculate gradient or Hessian matrix
get_hessian | Calculate gradient or Hessian matrix
get_hetsked_sstvar | Switch from two-regime reduced form STVAR model to a structural model identified by heteroskedasticity
get_soc | Calculate gradient or Hessian matrix
GFEVD | Estimate generalized forecast error variance decomposition for structural STVAR models.
GIRF | Estimate generalized impulse response function for structural STVAR models.
in_paramspace | Determine whether the parameter vector is in the parameter space
iterate_more | Maximum likelihood estimation of a reduced form or structural STVAR model based on preliminary estimates
linear_IRF | Estimate linear impulse response function based on a single regime of a structural STVAR model.
logLik.stvar | Create a class 'stvar' object defining a reduced form or structural smooth transition VAR model
LR_test | Perform likelihood ratio test for a STVAR model
plot.gfevd | Estimate generalized forecast error variance decomposition for structural STVAR models.
plot.girf | Estimate generalized impulse response function for structural STVAR models.
plot.irf | Estimate linear impulse response function based on a single regime of a structural STVAR model.
plot.stvar | Create a class 'stvar' object defining a reduced form or structural smooth transition VAR model
plot.stvarpred | Predict method for class 'stvar' objects
plot_struct_shocks | Plot structural shock time series of a STVAR model
Portmanteau_test | Perform adjusted Portmanteau test for a STVAR model
predict.stvar | Predict method for class 'stvar' objects
print.gfevd | Estimate generalized forecast error variance decomposition for structural STVAR models.
print.girf | Estimate generalized impulse response function for structural STVAR models.
print.hypotest | Print method for the class hypotest
print.irf | Estimate linear impulse response function based on a single regime of a structural STVAR model.
print.stvar | Create a class 'stvar' object defining a reduced form or structural smooth transition VAR model
print.stvarpred | Predict method for class 'stvar' objects
print.stvarsum | Summary print method from objects of class 'stvarsum'
profile_logliks | Plot profile log-likelihood functions about the estimates
Rao_test | Perform Rao's score test for a STVAR model
redecompose_Omegas | In the decomposition of the covariance matrices (Muirhead, 1982, Theorem A9.9), change the ordering of the covariance matrices.
reorder_B_columns | Reorder columns of impact matrix B of a structural STVAR model that is identified by heteroskedasticity or non-Gaussianity.
residuals.stvar | Create a class 'stvar' object defining a reduced form or structural smooth transition VAR model
simulate.stvar | Simulate method for class 'stvar' objects
sstvars | sstvars: toolkit for reduced form and structural smooth transition vector autoregressive models
STVAR | Create a class 'stvar' object defining a reduced form or structural smooth transition VAR model
stvar_to_sstvars110 | Update STVAR model estimated with a version of the package <1.1.0 to be compatible with the versions >=1.1.0.
summary.stvar | Create a class 'stvar' object defining a reduced form or structural smooth transition VAR model
swap_B_signs | Swap all signs in pointed columns of the impact matrix of a structural STVAR model that is identified by heteroskedasticity or non-Gaussianity
swap_parametrization | Swap the parametrization of a STVAR model
uncond_moments | Calculate the unconditional means, variances, the first p autocovariances, and the first p autocorrelations of the regimes of the model.

usacpu

A monthly U.S. data covering the period from 1987:4 to 2024:12 (453 observations) and consisting six variables. First, the climate policy uncertainty index (CPUI) (Gavridiilis, 2021), which is a news based measure of climate policy uncertainty. Second, the economic policy uncertainty index (EPUI), which is a news based measure of economic policy uncertainty, including also components quantifying the present value of future scheduled tax code expirations and disagreement among professional forecasters over future goverment purchases and consumer prices. Third, the log-difference of real indsitrial production index (IPI). Fourth, the log-difference of the consumer price index (CPI). Fifth, the log-difference of the producer price index (PPI). Sixth, an interest rate variable, which is the effective federal funds rate that is replaced by the the Wu and Xia (2016) shadow rate during zero-lower-bound periods. The Wu and Xia (2016) shadow rate is not bounded by the zero lower bound and also quantifies unconventional monetary policy measures, while it closely follows the federal funds rate when the zero lower bound does not bind. This is the dataset used in Virolainen (2025)

usamone

A quarterly U.S. data covering the period from 1954Q3 to 2021Q4 (270 observations) and consisting three variables: cyclical component of the log of real GDP, the log-difference of GDP implicit price deflator, 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.

Wald_test | Perform Wald test for a STVAR model