# alt_gsmvar: Construct a GMVAR, StMVAR, or G-StMVAR model based on results... In saviviro/gmvarkit: Estimate Gaussian or Student's t Mixture Vector Autoregressive Model

## Description

`alt_gsmvar` constructs a GMVAR, StMVAR, or G-StMVAR model based on results from an arbitrary estimation round of `fitGSMVAR`.

## Usage

 ```1 2 3 4 5 6 7``` ```alt_gsmvar( gsmvar, which_round = 1, which_largest, calc_cond_moments = TRUE, calc_std_errors = TRUE ) ```

## Arguments

 `gsmvar` an object of class `'gsmvar'`, typically created with `fitGSMVAR` or `GSMVAR`. `which_round` based on which estimation round should the model be constructed? An integer value in 1,...,`ncalls`. `which_largest` based on estimation round with which largest log-likelihood should the model be constructed? An integer value in 1,...,`ncalls`. For example, `which_largest=2` would take the second largest log-likelihood and construct the model based on the corresponding estimates. If used, then `which_round` is ignored. `calc_cond_moments` should conditional means and covariance matrices should be calculated? Default is `TRUE` if the model contains data and `FALSE` otherwise. `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 `GSMVAR` that picks the correct estimates from an object returned by `fitGSMVAR`.

## Value

Returns an object of class `'gsmvar'` defining the specified reduced form or structural GMVAR, StMVAR, or G-StMVAR model. Can be used to work with other functions provided in `gmvarkit`.

Note that the first autocovariance/correlation matrix in `\$uncond_moments` is for the lag zero, the second one for the lag one, etc.

## References

• Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.

• Kalliovirta L. and Saikkonen P. 2010. Reliable Residuals for Multivariate Nonlinear Time Series Models. Unpublished Revision of HECER Discussion Paper No. 247.

• Virolainen S. 2020. Structural Gaussian mixture vector autoregressive model. Unpublished working paper, available as arXiv:2007.04713.

• Virolainen S. 2021. Gaussian and Student's t mixture vector autoregressive model. Unpublished working paper, available as arXiv:2109.13648.

`fitGSMVAR`, `GSMVAR`, `iterate_more`, `update_numtols`
 ```1 2 3 4 5``` ```# GMVAR(1,2) model fit12 <- fitGSMVAR(gdpdef, p=1, M=2, ncalls=2, seeds=4:5) fit12 fit12_2 <- alt_gsmvar(fit12, which_largest=2) fit12_2 ```