HLRTest function

Hansen (1992) likelihood ratio test

Hansen (1992) likelihood ratio test

This function performs Hansen's likelihood ratio test as described in Hansen (1992). Original source code can be found here.

HLRTest(Y, p, control = list())

Arguments

  • Y: A (T x 1) matrix of observations.

  • p: Integer determining the number of autoregressive lags.

  • control: List with test procedure options including:

    • ix: List of Markov Switching parameters. 1 = just mean c(1,2) = mean and first param, (default: 1).
    • msvar: Boolean indicator. If TRUE, there is a switch in variance. If FALSE only switch in mean is considered. Default is FALSE.
    • qbound: Indicator that bounds q by 1-p (default: FALSE).
    • gridsize: Integer determining the number of grid points for markov switching parameters. Default is 20.
    • pgrid_from: Double determining the initial grid point for transition probabilities. Default is 0.1.
    • pgrid_by: Double determining the step size for grid points of transition probabilities. This, along with p_gridsize will determine the bounds of search space. Default is 0.075.
    • pgrid_to: Double determining the end grid point for transition probabilities. Default is 0.925.
    • mugrid_from: Double determining the minimum value of mean in second regime. Default is 0.1.
    • mugrid_by: Double determining the step size for grid points of mean in second regime. This, along with gridsize will determine the max value of mean in second regime. Default is 0.1.
    • siggrid_from: Double determining the minimum value of sigma in second regime (if msvar = TRUE). Default is 0.1.
    • siggrid_by: Double determining the step size for grid points of sigma in second regime. This, along with gridsize will determine the max value of sigma in second regime. Default is 0.1.
    • N: Integer determining the number of replications. Default is 1000.
    • nwband: Integer determining maximum bandwidth in Bartlett kernel. Critical values and p-values are returned for each bandwidth from 0:nwband as suggested in Hansen (1996). Default is 4.
    • theta_null_low: Vector determining lower bound on parameters under the null hypothesis. Length of vector should be number of model coefficients + 1 for variance. Default is to only bound variance at 0.01.
    • theta_null_upp: Vector determining upper bound on parameters under the null hypothesis. Length of vector should be number of model coefficients + 1 for variance. Default is to no bounds (i.e. Inf).
    • optim_method: String determining the type of optimization procedure used. Allowed options are "gp-optim" for general purpose optimization using optim from or slsqp. Default is "gp-optim".

Returns

List of class HLRTest (S3 object) with model attributes including:

  • mdl_h0: List with restricted model attributes. This will be of class ARmdl (S3 object). See ARmdl.
  • LR0: Likelihood ratio test statistic value.
  • LRN: A (N x 1) vector with simulated LRT statistics under null hypothesis.
  • pval: P-value.
  • LR_cv: A (nwband x 3) matrix with 90%, 95%, and 99% critical values in each column respectively.
  • coef: Vector of coefficients from restricted model and grid search that maximized standardized LRT.
  • control: List with test procedure options used.

Examples

# --------------------------- Use simulated process ---------------------------- set.seed(1234) # Define DGP of MS AR process mdl_ms2 <- list(n = 200, mu = c(5,1), sigma = c(1,1), phi = c(0.5), k = 2, P = rbind(c(0.90, 0.10), c(0.10, 0.90))) # Simulate process using simuMSAR() function y_ms_simu <- simuMSAR(mdl_ms2) hlrt_control <- list(ix = 1, gridsize = 7, pgrid_from = 0.05, pgrid_by = 0.05, pgrid_to = 0.95, mugrid_from = 0, mugrid_by = 1) hlrt <- HLRTest(y_ms_simu$y, p = 1, control = hlrt_control) summary(hlrt)

References

Hansen, Bruce E. 1992. “The likelihood ratio test under nonstandard conditions: testing the Markov switching model of GNP.” Journal of applied Econometrics 7 (S1): S61–S82.

Hansen, Bruce E. 1996. “Erratum: The likelihood ratio test under nonstandard conditions: testing the Markov switching model of GNP.” Journal of applied Econometrics 7 (S1): S61–S82.

  • Maintainer: Gabriel Rodriguez Rondon
  • License: GPL (>= 2)
  • Last published: 2025-02-24