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MSTest 0.1.9

Reproducibility note

• The internal standard-normal generator randSN() now uses arma::randn instead of a Box-Muller transform (faster, and avoids a log(0) edge case). IMPORTANT: this changes the random numbers drawn for a given set.seed(), so simulated series (the simu* functions) and Monte Carlo p-values (LMCLRTest(), MMCLRTest(), DLMCTest(), DLMMCTest(), CHPTest()) differ numerically from earlier versions. Test conclusions and validity are unaffected.

Testing procedures

• MMCLRTest(): the observed likelihood ratio statistic is now held fixed across the nuisance-parameter search, following Dufour (2006), instead of being recomputed at each candidate value. This corrects the maximized Monte Carlo p-value (which could previously be slightly liberal).
• MMCLRTest(): pre-drawn simulation innovations are now held fixed across the optimization (fixed-error Monte Carlo; Dufour 2006, Prop. 4.2).
• LMCLRTest() and MMCLRTest(): added an mc_seed control for fully reproducible Monte Carlo p-values; the number of parallel workers is capped at N when more workers than replications are requested.
• LMCLRTest() and MMCLRTest(): robust handling of failed simulated draws. Non-finite draws are dropped before the p-value is computed. If the simulated null distribution cannot be built at all (every draw fails even after the re-draw safety), the functions now stop with an informative message (increase use_diff_init or inspect the fit) instead of returning an invalid value or crashing the optimizer. In MMCLRTest(), a candidate parameter value whose null cannot be simulated is penalized so the optimizer avoids it; the initial value (theta_0) is exempt so that the MMC p-value remains at least as large as the LMC p-value.
• MMCLRTest(): the GA optimizer now uses popSize = 10 by default (GA’s own default of 50 makes the search perform 50 x maxit expensive evaluations); additional GA controls can be passed through optim_control.
• MCpval(): the type argument now accepts both long and short spellings ("geq", "leq", "two-tailed"/"two-tail", "absolute"/"abs") and raises an informative error for an unrecognized value (previously it silently returned a sentinel). The documentation now matches the accepted values.
• DLMMCTest(): fixed the sign of the stationarity penalty so non-stationary candidate parameters are correctly avoided by the optimizer (previously, in some cases the maximized p-value could be returned as the raw penalty constant).
• DLMCTest() and DLMMCTest(): corrected the sample size used to simulate the null distribution of the moment-based statistics. The simulated moments are now computed from samples of length T - p, matching the number of AR(p) residuals used for the observed statistic and for the p-value calibration in approxDistDL() (previously T). This restores the exact exchangeability underlying the Monte Carlo p-value (Dufour & Luger 2017); the effect is negligible for large samples and grows with p/T.
• print()/summary() for DLMMCTest objects now display the nuisance parameter value that maximizes the Monte Carlo p-value (theta_max_min/theta_max_prod) alongside the moment statistics, matching the output of DLMCTest (display only; the returned object is unchanged).

Estimation (EM)

• New conv control for the EM stopping criterion in HMmdl(), MSARmdl(), MSARXmdl(), MSVARmdl(), and MSVARXmdl(), with options "loglik" (relative log-likelihood change; the new default), "theta" (relative parameter change, the previous behavior), "both" (both, following Krolzig 1997), and "loglik-A"/"both-A" (Aitken-accelerated log-likelihood; Böhning et al. 1994, McLachlan and Krishnan 2008). A separate ltol control sets the log-likelihood tolerance (default 1e-7); thtol (default 1e-6) remains the parameter tolerance. NOTE: the default change from parameter-based stopping to "loglik" can change estimates slightly for a given set.seed(); set conv = "theta" to recover the previous behavior. Simulations show "loglik" matches the size and power of "theta" while converging substantially faster. Each fitted model now also reports a converged flag.
• Corrected the M-step mean update for Markov-switching AR and VAR models following Krolzig (1997) (the previous weighted-average update is exact only for hidden Markov models).
• Comprehensive numerical-stability improvements to the EM algorithm: guarded matrix inversions (solve() with fallback), Hamilton-filter underflow handling, degenerate-regime guards, positive-definite covariance regularization, and stationarity checks. Several data sets that previously triggered solve(): solution not found now estimate successfully.
• Faster Hamilton filter and Kim smoother (O(M) forward/backward recursions).
• Correct Kim-smoother handling for the transition-probability update (renormalization fix) and additional guards against transition-matrix corruption in degenerate replications.
• Fixed MSVARXmdl() to call MSVARXmdl_em() (not MSVARmdl_em()) in the single-initial-value EM branch (used when use_diff_init = 1 with a user-supplied init_theta), so exogenous regressors are handled correctly on that path.

Standard errors

• New se_method = "louis" control for the Markov-switching model constructors (HMmdl(), MSARmdl(), MSARXmdl(), MSVARmdl(), MSVARXmdl()), computing expected-complete-data standard errors (Louis 1982); automatic fallback from the observed-information Hessian to Louis when the Hessian is ill-conditioned.
• Transition-matrix standard errors now use a reduced (free-parameter) parameterization, fixing inflated or NA standard errors for k >= 2. Per-parameter step sizes are used in the numerical Hessian to keep transition probabilities within [0, 1].

Other

• Model constructors now validate use_diff_init >= 1 (and maxit_converge >= 1) with a clear error instead of failing cryptically.
• C++ sources: made integer/double literal usage explicit throughout (no change to results).
• Added a unit-test suite (testthat) and an introductory vignette.

MSTest 0.1.8

• made changes to some function examples.

MSTest 0.1.7

• Made change in trycatch error message for HLRTest() to better describe optimization issues.
• Made minor changes to article.R example file.

MSTest 0.1.6

• Updated MLE estimation following deprecation of hin>=0 (inequality constraint direction) in slsqr
• Changed OLS unbiased estimates of models with k=1 to be consistent with MLE estimates. Package is for testing more than estimation so comparison with MLE-based tetsing is prioritized.
• Changed use of arma::is_finite(X) to std::isfinite(X) because former is now deprecated.

MSTest 0.1.5

• patch to compile following (Rcpp) Armadillo update (i.e., added proper namespace scopeas_scalar).

MSTest 0.1.2

• Made change to MSARmdl(), MSVARmdl(), and HMmdl(). Specifically, when msmu or msvar is FALSE, output list copies single regime value for each k regime. This is needed for simulating the null when either msmu or msvar is FALSE in LMCLRTest() and MMCLRTest().
• Added option to use different number of initial values for estimating MSMs with observed data vs. for null distribution (see documentation for LMCLRTest() and MMCLRTest()).
• Updated USGNP data set to include 2022 Q3.
• MMCLRTest() now has the option to add lower and upper bounds for autoregressive coefficients and transition probabilities to help reduce errors from polyroot() when optimizing. MMC_bounds() has been updated to reflect this.
• DLMMCTest() now has the option to add lower and upper bounds for autoregressive coefficients to help reduce errors from polyroot() when optimizing. DLMMC_bounds() has been created for this.
• Fixed error in bootCV() used by CHPTest(). When NaN occurs, new draw is used.
• Added option to allow user to specify ‘mle_theta_low’ and ‘mle_theta_upp’ which determine the lower and upper bounds for optimization in HMmdl(), MSARmdl(), and MSVARmdl() when “method=‘MLE’” is specified.
• print.CHPTest() now used two lines to print description.
• In HLRTest() user can now define entire grid for transition probabilities.
• added new optional optimization routine for HLRparamSearch() (nloptr::slsqp() can be used now).
• Fixed bug in HLRTest() where grid for sigma options are properly used now.
• Now using nearest_spd() from pracma instead of nearPD() from lmf package.
• Added classes for simulation functions
• Added new methods, namely: coef, fitted, predict, summary, residuals, nobs, plot
• Made changes to print method.
  
• Changed methods for models, namely: logLiklihood now uses logLik, and aic and bic now use AIC and BIC methods.
• Updated USGNP dataset to include data up to end of 2023.
  
• Updated README.md file with usage of new methods.
• Updated DESCRIPTION file for changed dependencies and new version.

MSTest 0.1.1

• Made changes to MMC LRT related functions for obtaining null distribution of statistical test.
• Added more examples. Examples that take long to complete are commented out but serve to get familiar with usage.
• Fixed bug related to using init_theta (setting initial values of parameters) when estimating Markov models (i.e. MSARmdl(), HMmdl(), and MSVARmdl()).
• Fixed bug in MMC_bound() when k0>1
• Updated DESCRIPTION file for new version.

MSTest 0.1.0

• Added a NEWS.md file to track changes to the package.
• Added a README.md file to describe the package.
• First public version of package.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.