ma_increasing_order – MARX – moving averages of increasing order (Coulombe 2024).#

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Operational op under axis op, sub-layer L3_A_step_op, layer l3. Standalone callable: mf.functions.ma_increasing_order_transform.

Function signature#

mf.functions.ma_increasing_order_transform(
    panel: pd.DataFrame,
    max_order: int,
) -> pd.DataFrame

Parameters#

name

type

default

constraint

description

panel

pd.DataFrame

—

—

Input panel. Each column is a variable; rows are time periods. Series is promoted to a single-column DataFrame internally.

max_order

int

12

>= 2

Maximum window order. Generates windows 2, 3, …, max_order.

Returns#

pd.DataFrame — scalar result.

Behavior#

Stacks moving averages with windows [1, 2, 4, 8, ..., w_max] into a multi-column block. Captures multi-scale persistence in a single op; popular feature in macroeconomic random forest pipelines.

Implements the MARX (Moving-Average-of-Random-eXogeneous) trick from Coulombe (2024).

When to use

Tree / RF models that benefit from multi-scale temporal features without manual lag selection.

In recipe context#

Set params.op = "ma_increasing_order" in the relevant layer to activate this op within a recipe:

# Layer L3 recipe fragment
params:
  op: ma_increasing_order

References#

  • macroforecast design Part 2, L3: ‘feature engineering is a DAG of typed transforms; cascade-depth bounds the longest chain at cascade_max_depth.’

  • Coulombe (2024) ‘The Macroeconomic Random Forest’, Journal of Applied Econometrics 39(7): 1190-1209.