spa_test() now follows Hansen (2005). It previously
tested in the wrong direction (rejecting when the benchmark was best),
scaled the statistic by an extra sqrt(n), and did not use
Hansen’s consistent recentring. The hl2005 article is
updated: GARCH(1,1) is not rejected under any realised-variance proxy at
the 5% level.
cspa_test() now reports the correct p-value when the
null is rejected at every level (was 1, now 0) or at none (was 0, now
1). The reject decision was unaffected.
Added tests for cspa_test(), csms(),
cspa_test_plot(), do_prewhiten() and
get_legendre().
Refined the package title and DESCRIPTION text per
CRAN feedback: removed the redundant “Tools for” prefix from the title
and spelled out acronyms (MSFE, SPA) on first use.
Initial release. Provides a unified toolkit for out-of-sample forecast dominance testing.
dm_test() — Diebold-Mariano (with Harvey, Leybourne,
and Newbold
(1997) small-sample correction).cw_test() — Clark and West (2007) MSFE-adjusted
statistic.mse_f_test() — McCracken (2007) MSE-F equal-MSFE
statistic for nested out-of-sample comparison.enc_new() — ENC-NEW encompassing test of Clark and
McCracken (2001).gw_test() — Giacomini and White (2006) conditional
equal predictive ability test.spa_test() — Hansen (2005) test for superior predictive
ability.cspa_test() — Li, Liao, and Quaedvlieg (2022) test for
conditional superior predictive ability, with the accompanying
cspa_test_plot() visualisation and csms()
confidence set for the most superior method.uspa_mh_test() and aspa_mh_test() —
uniform and average multi-horizon SPA tests of Quaedvlieg (2021),
implemented with a moving-block bootstrap.ivx_wald() — Kostakis, Magdalinos, and Stamatogiannis
(2015) IVX-Wald statistic for predictive regressions with persistent
regressors.qll_hat() — Elliott and Muller (2006) qLL statistic for
testing parameter time variation.Bundled cm2001, hl2005,
gw2006, wg2008, quaedvlieg2021,
llq2022, llq2022_jnj,
llq2022_uv_cspa, nrtz2014,
rossi2006, rrz2016, and rz2013
datasets used by the replication vignettes.