| iterate_more {sstvars} | R Documentation |
Maximum likelihood estimation of a reduced form or structural STVAR model based on preliminary estimates
Description
iterate_more uses a variable metric algorithm to estimate a reduced form or structural STVAR model
(object of class 'stvar') based on preliminary estimates.
Usage
iterate_more(stvar, maxit = 100, calc_std_errors = TRUE)
Arguments
stvar |
an object of class |
maxit |
the maximum number of iterations in the variable metric algorithm. |
calc_std_errors |
should approximate standard errors be calculated? |
Details
The purpose of iterate_more is to provide a simple and convenient tool to finalize
the estimation when the maximum number of iterations is reached when estimating a STVAR model
with the main estimation function fitSTVAR or fitSSTVAR.
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.
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.
See Also
fitSTVAR, STVAR, optim,
swap_B_signs, reorder_B_columns
Examples
## These are long running examples that take approximately 20 seconds to run.
# Estimate two-regime Gaussian STVAR p=1 model with the weighted relative stationary densities
# of the regimes as the transition weight function, but only 5 iterations of the variable matrix
# algorithm:
fit12 <- fitSTVAR(gdpdef, p=1, M=2, nrounds=1, seeds=1, ncores=1, maxit=5)
# The iteration limit was reached, so the estimate is not local maximum.
# The gradient of the log-likelihood function:
get_foc(fit12) # Not close to zero!
# So, we run more iterations of the variable metric algorithm:
fit12 <- iterate_more(fit12)
# The gradient of the log-likelihood function after iterating more:
get_foc(fit12) # Close to zero!