Nothing
mbh_filter()'s automatic knot count is now capped at 250
(min(max(20, floor(n / 2)), 250)). Series of 500 observations or fewer are
unaffected; the cap only bounds the B-spline basis for long or high-frequency
inputs, where extra knots inflate memory and runtime without adding
flexibility (in a P-spline the difference penalty, not the knot count,
controls smoothness).d = "auto" default is calibrated from the MAD of the HP cyclical residual
(not first differences), and the default learning rate is nu = 0.1.hp_filter(), hamilton_filter(), bhp_filter(), mbh_filter()). The new
boot_iter and block_size arguments add $trend_lower / $trend_upper
to the result: a 95% normal-approximation band (trend ± 1.96 * sd) built
from a Circular Block Bootstrap of the cycle, with each replicate refit by the
same estimator as the base fit.autoplot() method for macrofilter objects (ggplot2): draws the
observed series, the estimated trend, and the confidence ribbon when present,
with the time axis reconstructed from the stored temporal identity.mbh_filter() gains hp_lambda to control the HP-based auto-calibration of
the Huber threshold d when the input is a plain numeric vector whose true
frequency is not annual.hp_filter() and
bhp_filter() with boot_iter > 0 (and the base bHP fit), with bit-identical
results.d = "auto" calibration in mbh_filter() now uses the MAD of the HP
cyclical residual (output-gap scale) instead of mad(diff(y)), and reports
the chosen value via a message().c("macrofilter", "list") and store the
temporal identity (meta$ts_class, meta$tsp, meta$idx) so trend, cycle
and bands can all be mapped back to dates for plotting.boot_iter,
block_size, the end-point fan and the Hamilton conditional band.mbh_filter() documents the mstop–d interaction (reducing mstop on long
log-level series under-smooths the trend); hamilton_filter() documents the
conditional bootstrap band behaviour.Any scripts or data that you put into this service are public.
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