mbh_filter: MacroBoost Hybrid (MBH) Filter

In MacroFilters: Robust Trend-Cycle Decomposition for Macroeconomic Time Series

MacroBoost Hybrid (MBH) Filter

Description

Decomposes a time series into trend and cycle using a robust boosting algorithm. Unlike the HP filter, MBH uses the Huber loss function to automatically downweight outliers (like the COVID-19 shock), preventing them from distorting the trend.

Usage

mbh_filter(
  x,
  d = "auto",
  boot_iter = 0,
  block_size = "auto",
  knots = NULL,
  mstop = 500L,
  nu = 0.1,
  df = 4L,
  select_mstop = FALSE,
  boundary.knots = NULL,
  hp_lambda = NULL
)

Arguments

x	Numeric vector, ts, xts, or zoo object.
d	Numeric or "auto". The delta parameter for Huber loss. If "auto" (default), it is calibrated as stats::mad(.hp_fast(x)), i.e. the MAD of the HP cyclical residual. This anchors the threshold to the output-gap scale rather than the growth-rate scale, avoiding the under-truncation failure mode of the legacy mad(diff(y)) heuristic. A message() is emitted reporting the exact value chosen. Supply an explicit positive numeric to override.
boot_iter	Non-negative integer. Number of block-bootstrap iterations for uncertainty quantification (default 0, bootstrap disabled). When ⁠> 0⁠, the function adds ⁠$trend_lower⁠ and ⁠$trend_upper⁠: a 95% normal-approximation band, ⁠trend +/- 1.96 * sd(bootstrap trends)⁠, centred on the estimated trend. The bootstrap sd is used instead of empirical percentiles because it is smooth and stable at practical boot_iter. Each bootstrap refit uses the same mstop as the base fit, so larger boot_iter raises cost linearly. See also block_size.
block_size	Positive integer or "auto". Block length for the moving-block bootstrap (used only when boot_iter > 0). If "auto" (default), it is set to 2 * stats::frequency(x) (two full cycles), bounded above by floor(length(x) / 3) to keep at least three blocks.
knots	Integer. Number of interior knots for the P-Spline. If NULL (default), it is calculated as min(max(20, floor(n / 2)), 250). High knot density keeps the trend flexible, while the cap of 250 keeps the B-spline basis bounded for long / high-frequency series: in a P-spline the smoothness is governed by the difference penalty (via df, mstop, nu), not by the knot count, so beyond a few hundred knots the extra basis columns only inflate memory and runtime without adding useful flexibility.
mstop	Integer. Maximum number of boosting iterations (default 500). If select_mstop = TRUE this is the upper bound; the actual stopping point is chosen by AICc. Under-smoothing warning (mstop vs d): when d is small relative to the trend's range – the typical case for long log-level series, where the cycle (hence the auto-calibrated d) is tiny but the trend spans a large range – the Huber loss caps the gradient from the first iteration, so each boosting step advances the trend only slightly. Reducing mstop then leaves the trend unable to climb its full range: it collapses to a nearly flat curve while the cycle absorbs the long-run variation. Keep the default mstop = 500 (or higher) for such series; lower it only for short or high-cycle-variance inputs.
nu	Numeric. The learning rate (shrinkage) for boosting (default 0.1).
df	Integer. Effective degrees of freedom per boosting step for the P-Spline base learner (default 4). This enforces the weak-learner constraint of Bühlmann & Hothorn (2007): each boosting step contributes only a small, smooth update so that the trend is built up gradually over many iterations rather than fitted in one pass. End-point instability warning: Higher df values cause the B-spline basis matrix to shift drastically when the sample size changes by even one observation (the "rubber-band effect"). The last few data points pull the estimated trend non-smoothly, producing unreliable end-of-sample estimates. Keep df = 4 (the default) unless you have a specific reason to deviate.
select_mstop	Logical. If TRUE, the optimal number of boosting iterations is selected automatically via AICc (corrected AIC), following Bühlmann & Hothorn (2007). The mstop argument acts as the search upper bound. Default FALSE. AICc underfitting warning: In the combination of Huber quasi-likelihood + P-splines, AICc penalises model complexity hyper-aggressively. In practice the algorithm stops at iteration ~5–15 instead of the intended ~500. The resulting trend is nearly a straight line; all long-run variance is pushed into the cycle component, defeating the purpose of the filter. Treat select_mstop = TRUE as an experimental option and validate visually before relying on it.
boundary.knots	A numeric vector of length 2 specifying the global domain for the B-spline basis (e.g., c(1, T_max)). If NULL (default), the range of time_idx is used. For real-time stability, fix this to the full-sample domain so the basis does not shift as the sample grows.
hp_lambda	Numeric or NULL. Smoothing parameter for the internal HP filter used to auto-calibrate d (only relevant when d = "auto"). If NULL (default), it is derived from stats::frequency(x) via the Ravn-Uhlig rule. Supply this when x is a plain numeric vector whose true frequency is not annual, since frequency() returns 1 for unclassed vectors and would otherwise under-smooth the calibration cycle (e.g. monthly data: hp_lambda = 129600).

Details

The model estimated is an additive model:

y_t = \text{Linear}(t) + \text{Smooth}(t) + \epsilon_t

It is fitted using mboost::mboost() with:

• Base Learners: A linear time trend (mboost::bols()) to capture the global path, plus a B-spline (mboost::bbs()) to capture local curvature.

• Loss Function: Huber loss (mboost::Huber()) with parameter d. This is the key to robustness.

The default parameters (knots = min(n/2, 250), mstop = 500) are calibrated to mimic the flexibility of a standard HP filter while retaining the robustness of the Huber loss.

Value

A list of class c("macrofilter", "list") with:

⁠$trend⁠

Numeric trend vector.

⁠$cycle⁠

Numeric cycle vector.

⁠$data⁠

Original input as numeric.

⁠$meta⁠

Named list: method, knots, d, mstop, nu, df, select_mstop, compute_time.

⁠$trend_lower⁠, ⁠$trend_upper⁠

95% normal-approximation bootstrap band (⁠trend +/- 1.96 * sd⁠). Present only when boot_iter > 0.

Calibration Guidance

Three failure modes were discovered through empirical stress-testing. The defaults guard against all three:

1. Huber delta scale mismatch (d)

The automatic fallback mad(diff(y)) operates on the scale of growth rates, not the output gap. For log-level input this sets d one to two orders of magnitude too small, causing ordinary business-cycle swings to be treated as outliers. If the estimated cycle looks implausibly large or the trend is nearly linear, override with d = mad(hp_filter(x)$cycle) as a starting point.

2. AICc underfitting (select_mstop)

AICc + Huber quasi-likelihood + P-splines stops boosting at iteration ~5–15. The trend degenerates to a near-straight line and the cycle absorbs all long-run variance. Leave select_mstop = FALSE (the default) and set mstop explicitly instead.

3. End-point instability (df)

Values above 4 shift the B-spline basis matrix non-smoothly as the sample grows, producing a "rubber-band" distortion in the final observations. Keep df = 4 (the default) for real-time applications.

Examples

# Fast example with reduced series and iterations
set.seed(42)
y <- ts(cumsum(rnorm(80)), start = c(2000, 1), frequency = 4)
result <- mbh_filter(y, mstop = 100L)
print(result)


# Full example with default parameters
y2 <- ts(cumsum(rnorm(200)), start = c(2000, 1), frequency = 4)
result2 <- mbh_filter(y2)
print(result2)

MacroFilters documentation built on June 12, 2026, 1:06 a.m.