Macroeconomic model reference

MIDAS Model

Mixed-data sampling regressions that handle indicators arriving at different frequencies inside a single forecasting equation.

Empirical forecasting models · Sources

MIDAS sources, papers, and evidence trail

Primary papers, model variants, source notes, and review signals behind the MIDAS page.

Staff papers, NBER-style drafts, and research notes used where the live literature has not fully settled.

[S1] Working paper
Ghysels, Santa-Clara, and Valkanov (2004) 'The MIDAS Touch: Mixed Data Sampling Regression Models': the original working paper introducing the MIDAS framework.
Working paper

Reference material used for orientation; read primary and academic sources first when claims conflict.

[S2] Reference
Ghysels, Santa-Clara, and Valkanov (2006) 'Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies': applied MIDAS to volatility forecasting, demonstrating gains from daily-to-monthly frequency mismatch.
Reference
[S3] Reference
Foroni, Marcellino, and Schumacher (2015) 'Unrestricted Mixed Data Sampling (MIDAS): MIDAS Regressions with Unrestricted Lag Polynomials': introduced U-MIDAS for low frequency ratios where free coefficients are feasible.
Reference
[S4] Reference
Andreou, Ghysels, and Kourtellos (2013) 'Should Macroeconomic Forecasters Use Daily Financial Data and How?': systematic evaluation of daily-to-quarterly MIDAS for macro forecasting.
Reference
[S5] Reference
Clements and Galvao (2008) 'Macroeconomic Forecasting with Mixed-Frequency Data: Forecasting Output Growth in the United States': compared MIDAS against bridge equations and direct multi-step forecasting.
Reference

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Data-Driven Models

References

Staff papers, NBER-style drafts, and research notes used where the live literature has not fully settled.

[S1] Working paper
Ghysels, Santa-Clara, and Valkanov (2004) 'The MIDAS Touch: Mixed Data Sampling Regression Models': the original working paper introducing the MIDAS framework.
Working paper

Reference material used for orientation; read primary and academic sources first when claims conflict.

[S2] Reference
Ghysels, Santa-Clara, and Valkanov (2006) 'Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies': applied MIDAS to volatility forecasting, demonstrating gains from daily-to-monthly frequency mismatch.
Reference
[S3] Reference
Foroni, Marcellino, and Schumacher (2015) 'Unrestricted Mixed Data Sampling (MIDAS): MIDAS Regressions with Unrestricted Lag Polynomials': introduced U-MIDAS for low frequency ratios where free coefficients are feasible.
Reference
[S4] Reference
Andreou, Ghysels, and Kourtellos (2013) 'Should Macroeconomic Forecasters Use Daily Financial Data and How?': systematic evaluation of daily-to-quarterly MIDAS for macro forecasting.
Reference
[S5] Reference
Clements and Galvao (2008) 'Macroeconomic Forecasting with Mixed-Frequency Data: Forecasting Output Growth in the United States': compared MIDAS against bridge equations and direct multi-step forecasting.
Reference