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 |
fitSSTVAR |
Maximum likelihood estimation of a structural STVAR model based on preliminary estimates from a reduced form model. |
fitSTVAR |
Two-phase maximum likelihood estimation of a reduced form smooth transition VAR model |
GAfit |
Genetic algorithm for preliminary estimation of a 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 |
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 (and lambda parameters if any) 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 |
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 2024:2 (443 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. 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. |
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 |