Staggered-Combination MIDAS

Mixed-frequency nowcasting and short-horizon forecasting of quarterly targets (e.g. GDP) from monthly indicators, with forecast combination.

Experimental Python implementation of Moreira (2025)

Read the user manual for the full guide.

Features

• MIDAS regression with Almon, exponential-Almon, Beta and unrestricted lag polynomials.
• Quarterly OLS counterpart (OLS / OLSSpec) for hard quarterly indicators, sharing the same forecasting and dummy interface.
• Direct multi-horizon forecasting: one model per horizon h, fit and stored together.
• Outlier dummies on the target (dummy_periods) absorbed at the target frequency.
• End-to-end combination pipeline MidasCombo:
  • Average, inverse-error (mae, mse, rmse) and regression weights across indicators.
  • Rolling-window and discounted-error variants.
  • Combinations of combinations (a ComboSpec can reference other ComboSpecs as sources).
• NLS estimation of non-linear weight schemes (Almon-exp, Beta).

Project Structure

├── src/nowcast_midas/    # Source code
├── docs/            # Zensical documentation site
├── examples/        # Example scripts
├── tests/midas/     # Unit tests
└── ...

Installation

pip install -e .                   # runtime
pip install -e ".[dev]"            # + test / lint tooling
pip install -e ".[docs]"           # + Zensical docs build

Python ≥ 3.10.

Quick start

from nowcast_midas import MidasCombo, MidasSpec, OLSSpec, ComboSpec
from nowcast_midas.utils import sample_combo_data

# Simulated data: three monthly series, one quarterly regressor, and a quarterly target.
target, regressors, info = sample_combo_data(n_quarters=60, seed=42)
outlier = info["outlier_date"]

# Two monthly MIDAS indicators and one quarterly OLS indicator.
midas_monthly_1 = MidasSpec(
    "monthly_1", method="almon", n_lags=6, dummy_periods=[outlier]
)
midas_monthly_2 = MidasSpec(
    "monthly_2", method="almon", n_lags=6, dummy_periods=[outlier]
)
ols_quarterly_1 = OLSSpec("quarterly_1", n_lags=1, dummy_periods=[outlier])

# Two MIDAS models combined via inverse-MSE weights ...
soft_combo = ComboSpec(
    name="soft_combo",
    sources=[midas_monthly_1, midas_monthly_2],
    method="mse",
    window=8,
    discount_rate=0.95,
)

# ... then merged with a quarterly OLS model via constrained regression.
final_combo = ComboSpec(
    name="final_combo",
    sources=[soft_combo, ols_quarterly_1],
    method="regression",
)

model = MidasCombo(combo_specs=final_combo, horizons=3)

model.fit(target=target, regressors=regressors)
oos = model.forecast()  # wide table: one column per (variable, horizon)
print(model.summary(horizon=0))

Selected documentation

• SC-MIDAS framework
• Weighting schemes
• MIDAS model
• SC-MIDAS pipeline
• Real-time analysis

Selected examples

• MIDAS, MultiMIDAS, and SC-MIDAS
• SC-MIDAS with forecast-realtime

Contributing

• See CONTRIBUTING.md for details on how to contribute to the code.
• Open an issue with questions or ideas.

Main references

• Moreira (2025)
• Ghysels, Santa-Clara and Valkanov (2004)
• Ghysels, Sinko and Valkanov (2007)

Data Classification

Bank of England Data Classification: OFFICIAL BLUE