gmvarkit (version 2.1.4)

LR_test: Perform likelihood ratio test for a GMVAR, StMVAR, or G-StMVAR model

Description

LR_test performs a likelihood ratio test for a GMVAR, StMVAR, or G-StMVAR model

Usage

LR_test(gsmvar1, gsmvar2)

Value

A list with class "hypotest" containing the test results and arguments used to calculate the test.

Arguments

gsmvar1

an object of class 'gsmvar' generated by fitGSMVAR or GSMVAR, containing the freely estimated model.

gsmvar2

an object of class 'gsmvar' generated by fitGSMVAR or GSMVAR, containing the constrained model.

Details

Performs a likelihood ratio test, testing the null hypothesis that the true parameter value lies in the constrained parameter space. Under the null, the test statistic is asymptotically \(\chi^2\)-distributed with \(k\) degrees of freedom, \(k\) being the difference in the dimensions of the unconstrained and constrained parameter spaces.

Note that this function does not verify that the two models are actually nested.

References

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

• Virolainen S. (forthcoming). A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics.

• Virolainen S. 2022. Gaussian and Student's t mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks in the Euro area. Unpublished working paper, available as arXiv:2109.13648.

@keywords internal

See Also

Wald_test, Rao_test, fitGSMVAR, GSMVAR, diagnostic_plot, profile_logliks, quantile_residual_tests, cond_moment_plot

Examples

# \donttest{
 ## These are long running examples that use parallel computing!
 ## The below examples take around 1 minute to run.

 # Structural GMVAR(2, 2), d=2 model with recursive identification
 W22 <- matrix(c(1, NA, 0, 1), nrow=2, byrow=FALSE)
 fit22s <- fitGSMVAR(gdpdef, p=2, M=2, structural_pars=list(W=W22),
                     ncalls=1, seeds=2)

 # The same model but the AR coefficients restricted to be the same
 # in both regimes:
 C_mat <- rbind(diag(2*2^2), diag(2*2^2))
 fit22sc <- fitGSMVAR(gdpdef, p=2, M=2, constraints=C_mat,
                      structural_pars=list(W=W22), ncalls=1, seeds=1)

 # Test the AR constraints with likelihood ratio test:
 LR_test(fit22s, fit22sc)
 # }