log_diff – Log first difference: ln(y_t) - ln(y_{t-1}).#

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

Function signature#

mf.functions.log_diff_transform(
    panel: pd.DataFrame,
    periods: 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.

periods

int

1

>= 1

Number of lag periods to difference after taking logs.

Returns#

pd.DataFrame — scalar result.

Behavior#

Composite of log then diff. The standard FRED-MD transformation code 5/6 representation; produces a stationary approximation of the percentage change and is symmetric in expansions vs contractions.

When to use

Strictly-positive trending series (real GDP, employment, prices); FRED-MD tcode 5/6 default.

When NOT to use

Series that take non-positive values.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: log_diff

References#

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

  • McCracken & Ng (2016) ‘FRED-MD: A Monthly Database for Macroeconomic Research’, JBES 34(4): 574-589. https://doi.org/10.1080/07350015.2015.1086655