- Added a
NEWS.md file to track changes to the
- New exported functions: get_regime_means, get_regime_autocovs,
get_regime_vars, uncond_moments, get_soc, cond_moments,
- Generally more functionality for conditional and unconditional
moments, and convenient tools for switching to G-StMAR model from a
- Implemented an algorithm by Monahan (1984) to the genetic algorithm
for more thorough search of the parameter space near boundaries of the
- simulateGSMAR now provides better tools for forecasting. This update
includes non-backward compatible changes for the return values if the
argument ntimes is set to be larger than one. In additional to the
samples, it now returns a list containing the mixing weights and
component that was used to generate each observation.
- The arguments nCalls and nCores in fitGSMAR are now changed to
ncalls and ncores for consistency.
- Fixes on minor bugs that used to cause errors in some special
- Updates on documentation
- Added reference for the G-StMAR model.
- In the predict method arguments “ci” and “ci_type” were changed to
“pi” and “pi_type” to signify “prediction interval”” as it’s more
correct expression than “confidence interval”. Also the default
prediction method is now median, and not mean.
- Changed the default number of CPU cores employed by the estimation
function fitGSMAR to be at most two due to CRAN policy.
- Added the argument “seeds” to fitGSMAR allowing one to set the
random number generator seed for each call to the genetic
- Finite difference approximations for differentials regarding overly
large degrees of freedom parameters now give reasonable approximation
instead of numerical error.
- The maximum value for degrees of freedom parameters is now 1e5.
- New exported function alt_gsmar that conveniently constructs a GSMAR
model based on an arbitrary estimation round of fitGSMAR.
- New exported function get_foc which is the same as get_gradient but
with convenient name.
- The default number of generations in the genetic algorithm is now
200 (was min(400, max(round(0.1*length(data)), 200)) before).
- In various functions, user may now adjust the difference ‘h’ used in
the finite difference approximations for differentials of the
- Bug fix: the summary print for gsmar objects falsely displayed
standard error for the non-parametrized mixing weight
- Fixed typos etc. in documentation.
- Fixed ‘additional issue’ in CRAN checks
- New function: ‘profile_logliks’ for plotting profile log-likelihood
- Disabled camelCase compatibility for arguments ‘ncalls’ and ‘ncores’
- Updated the ‘regime combining procedure’ in the genetic algorithm to
also support the G-StMAR model.
- Minor computation speed improvements.
- Tidier code for some parts.
- Improved comments and documentation.
- Bug fix: the function ‘add_data’ did not identify the model type
- Bug fix: simulateGSMAR simulated some initial values from sligthly
wrong distribution; did not have affect on forecasts.
- Minor update on the summary print for the models
- Updated the plot method for class ‘gsmar’ objects: now includes a
density plot by default (can be removed).
- Updated the predict method for class ‘gsmar’ objects: now includes
predictions for the mixing weights (can be removed from the plot).
- Fixed ‘profile_logliks’ to show correct headlines with mean
parametrization + improved the default method for choosing the number of
rows and columns in the plot-matrix.
- Now standard errors are printed correctly for models imposing all
kinds of constraints. In the earlier versions, constrained AR parameter
standard errors were printed incorrectly if the constraints involved
multiplications or summations.
- Removed redundant reinitialization of a PSOCK cluster in the
- In the function quantile_residual_tests the default argument for
‘nsimu’ is now 1 so that the tests are based on the given data only (and
not on simulation).
- Added interest rate spread (10-Year minus 1-Year treasury)
- Bug fix: the predict method incurred an error when plotting the
results with n_ahead=1.
- New exported function: ‘cond_moment_plot’ for further visualization
of the model.
- Yet another bug fix: the predict method incurred an error with
- Corrected degrees of freedom labels for G-StMAR models in the
- Updated the examples.
- Major speed improvement!
- New exported function: ‘Wald_test’ for performing a Wald test.
- New exported function: ‘LR_test’ for performing a likelihood ratio
- The default lags in quantile residual tests are now 1, 3, 6, and
- Fixed some typos in documentation.
- This update (finally) renames functions and arguments so that they
are consistent throughout uGMAR and in line with the package “gmvarkit”.
Namely, some functions were renamed from camelCase to lower_bar
convention for consistency. Old functions are (for now) retained as
deprecated. Also, some arguments were renamed from camelCase to
lower_bar: print_res in fitGSMAR; print_res, lags_ac, and lags_ch in
quantile_residual_tests; smart_mu, mean_scale, and sigma_scale in GAfit;
plot_res in predict.gsmar; init_values in simulateGSMAR; and
- The package ‘gsl’ is now imported, and not a suggested package
anymore, to ensure fast calculation of quantile residual tests for StMAR
and G-StMAR models.
- The function random_ind does not sort components anymore when
constraints are employed (unless only the argument “restricted” is
used). Consequently, estimation results (with a specific seed) might
differ from previous versions for the models employing constraints.
- Removed to possibility to run quantile residual tests directly with
the estimation function after the estimation, because it is a good
practice to check first whether the estimates are appropriate. The tests
can be ran afterwards with the function “quantile_residual_tests”.
- fitGSMAR now warns if some regime is almost nonstationary.
- The function “stmar_to_gstmar”” now supports also G-StMAR models
with large degrees of freedom estimates.
- The summary method for class ‘gsmar’ objects now supports models
- Fixed a bug in the one-step conditional variance of the process: it
was incorrect in the previous versions.
- Updated the examples.
- Adjusted the plot methods.
- Added new data: M10Y1Y
- New, improved vignette.
- Now the functions “iterate_more” and “alt_gsmar” also warn about
near-unit-roots and return results from all the estimation rounds.
- Summary and print methods now display the number of parameters and
- Updated the reference to the StMAR model
- Updated some of the documentation
- fitGSMAR does not call closeAllConnections() on exit anymore;
instead, it only closes the connections it opened.
- Fixed the argument “precission” in profile_logliks to
- Added a new appendix to the vignette.
- Added more comments to the source code.
- Added new data “TBFF”: the interest rate spread between the 3-month
Treasury bill rate and the effective federal funds rate that was studied
in the empirical application of Virolainen (2021) introducing the
- Adjusted graphics in the function diagnostic_plot
- Updated the reference information of the G-StMAR model.
- Implemented the S3 method ‘simulate’ for class ‘gsmar’ objects.
Consequently, the function ‘simulateGSMAR’ is now deprecated.
- Re-used the class ‘htest’ for the objects returned by the functions
Wald_test and LR_test.
- In the printout of GMAR type regimes, the variance parameter is now
reported separately from the AR-equation. Also changed the “cond_sd” in
front of eps in the StMAR regimes to sigma_mt.
- Removed the documentation of internal functions from the
- Added legend to the density plot in the plot method for class
‘gsmar’ objects. Also adjusted the legend of the mixing weights
- Adjusted the legend in the mixing weights plot in the plot method
for class ‘gsmarpred’ objects.
- Updated doi in the reference to the G-StMAR model (Virolainen,
- Updated the vignette.
- Added URL for bug reports.
- Updated the vignette.
- Fixed a CRAN issue.
- Updated the vignette.