filter_estimates: Filter inappropriate the estimates produced by fitSTVAR

In sstvars: Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

Filter inappropriate the estimates produced by fitSTVAR

Description

filter_estimates filters out inappropriate estimates produced by fitSTVAR: can be used to obtain the (possibly) appropriate estimate with the largest found log-likelihood (among possibly appropriate estimates) as well as (possibly) appropriate estimates based on smaller log-likelihoods.

Usage

filter_estimates(
  stvar,
  which_largest = 1,
  filter_stab = TRUE,
  calc_std_errors = FALSE
)

Arguments

stvar	a class 'stvar' object defining a structural STVAR model that is identified by heteroskedasticity or non-Gaussianity, typically created with fitSSTVAR.
which_largest	an integer at least one specifying the (possibly) appropriate estimate corresponding to which largest log-likelihood should be returned. E.g., if which_largest=2, the function will return among the estimates that it does not deem inappropriate the one that has the second largest log-likelihood.
filter_stab	Should estimates close to breaking the usual stability condition be filtered out?
calc_std_errors	should approximate standard errors be calculated?

Details

The function goes through the estimates produced by fitSTVAR and checks which estimates are deemed inappropriate. That is, estimates that are not likely solutions of interest. Specifically, solutions that incorporate a near-singular error term covariance matrix (any eigenvalue less than 0.002), any modulus of the eigenvalues of the companion form AR matrices larger than $0.9985$ (indicating the necessary condition for stationarity is close to break), or transition weights such that they are close to zero for almost all t for at least one regime. Then, among the solutions are not deemed inappropriate, it returns a STVAR models based on the estimate that has the which_largest largest log-likelihood.

The function filter_estimates is kind of a version of alt_stvar that only considers estimates that are not deemed inappropriate

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.
seeds	The seeds used in the estimation in fitSTVAR, if applicable.
LS_estimates	The least squares estimates of the parameters in the form (\phi_{1},...,\phi_{M},\varphi_1,...,\varphi_M,\alpha (intercepts replaced by unconditional means if mean parametrization is used), if applicable.

See Also

fitSTVAR, alt_stvar

Examples

 # Fit a two-regime STVAR model with logistic transition weights and Student's t errors,
 # and use two-phase estimation method:
 fit12 <- fitSTVAR(gdpdef, p=1, M=2, weight_function="logistic", weightfun_pars=c(2, 1),
  cond_dist="Student", nrounds=2, ncores=2, seeds=1:2, estim_method="two-phase")
 fit12

 # Filter through inappropriate estimates and obtain the second best appropriate solution:
 fit12_2 <- filter_estimates(fit12, which_largest=2)
 fit12_2 # The same model since the two estimation rounds yielded the same estimate

sstvars documentation built on April 11, 2025, 5:47 p.m.