R: Construct a STVAR model based on results from an arbitrary...

alt_stvar {sstvars}

R Documentation

Construct a STVAR model based on results from an arbitrary estimation round of `fitSTVAR`

Description

alt_stvar constructs a STVAR model based on results from an arbitrary estimation round of fitSTVAR

Usage

alt_stvar(stvar, which_largest = 1, which_round, calc_std_errors = FALSE)

Arguments

`stvar`	object of class `"stvar"`
`which_largest`	based on estimation round with which largest log-likelihood should the model be constructed? An integer value in 1,...,`nrounds`. For example, `which_largest=2` would take the second largest log-likelihood and construct the model based on the corresponding estimates.
`which_round`	based on which estimation round should the model be constructed? An integer value in 1,...,`nrounds`. If specified, then `which_largest` is ignored.
`calc_std_errors`	should approximate standard errors be calculated?

Details

It's sometimes useful to examine other estimates than the one with the highest log-likelihood. This function is wrapper around STVAR that picks the correct estimates from an object returned by fitSTVAR.

Value

Returns an S3 object of class 'stvar' defining a smooth transition VAR model. The returned list contains the following components (some of which may be NULL depending on the use case):

`data`	The input time series data.
`model`	A list describing the model structure.
`params`	The parameters of the model.
`std_errors`	Approximate standard errors of the parameters, if calculated.
`transition_weights`	The transition weights of the model.
`regime_cmeans`	Conditional means of the regimes, if data is provided.
`total_cmeans`	Total conditional means of the model, if data is provided.
`total_ccovs`	Total conditional covariances of the model, if data is provided.
`uncond_moments`	A list of unconditional moments including regime autocovariances, variances, and means.
`residuals_raw`	Raw residuals, if data is provided.
`residuals_std`	Standardized residuals, if data is provided.
`structural_shocks`	Recovered structural shocks, if applicable.
`loglik`	Log-likelihood of the model, if data is provided.
`IC`	The values of the information criteria (AIC, HQIC, BIC) for the model, if data is provided.
`all_estimates`	The parameter estimates from all estimation rounds, if applicable.
`all_logliks`	The log-likelihood of the estimates from all estimation rounds, if applicable.
`which_converged`	Indicators of which estimation rounds converged, if applicable.
`which_round`	Indicators of which round of optimization each estimate belongs to, if applicable.

References

Anderson H., Vahid F. 1998. Testing multiple equation systems for common nonlinear components. Journal of Econometrics, 84:1, 1-36.
Hubrich K., Teräsvirta. T. 2013. Thresholds and Smooth Transitions in Vector Autoregressive Models. CREATES Research Paper 2013-18, Aarhus University.
Lanne M., Virolainen S. 2024. A Gaussian smooth transition vector autoregressive model: An application to the macroeconomic effects of severe weather shocks. Unpublished working paper, available as arXiv:2403.14216.
Kheifets I.L., Saikkonen P.J. 2020. Stationarity and ergodicity of Vector STAR models. Econometric Reviews, 39:4, 407-414.
Lütkepohl H., Netšunajev A. 2017. Structural vector autoregressions with smooth transition in variances. Journal of Economic Dynamics & Control, 84, 43-57.
Tsay R. 1998. Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93:443, 1188-1202.
Virolainen S. 2024. Identification by non-Gaussianity in structural threshold and smooth transition vector autoregressive models. Unpublished working paper, available as arXiv:2404.19707.

Examples


## These are long-running examples that take approximately 10 seconds to run.

# Estimate a Gaussian STVAR p=1, M=2 model with exponential weight function and
# the first lag of the second variable as the switching variables. Run only two
# estimation rounds:
fit12 <- fitSTVAR(gdpdef, p=1, M=2, weight_function="exponential", weightfun_pars=c(2, 1),
 nrounds=2, seeds=c(1, 7))
fit12$loglik # Log-likelihood of the estimated model

# Print the log-likelihood obtained from each estimation round:
fit12$all_logliks

# Construct the model based on the second largest log-likelihood found in the
# estimation procedure:
fit12_alt <- alt_stvar(fit12, which_largest=2, calc_std_errors=FALSE)
fit12_alt$loglik # Log-likelihood of the alternative solution

# Construct a model based on a specific estimation round, the second round:
fit12_alt2 <- alt_stvar(fit12, which_round=2, calc_std_errors=FALSE)
fit12_alt2$loglik # Log-likelihood of the alternative solution

[Package sstvars version 1.0.1 Index]

Construct a STVAR model based on results from an arbitrary estimation round of fitSTVAR