Research

Supervised Mixed-Frequency Learning for Macro-Financial Forecasting When Factors are Weak, with Ulrich Hounyo (Job Market Paper) 

Abstract:  Factor-MIDAS regression models are used to forecast a target variable by extracting common factors from a large panel of high-frequency predictors using principal component analysis (PCA). While PCA mitigates the curse of dimensionality, it relies on the pervasiveness of factors, an assumption often violated in practice when weak factors are present, particularly in macro-financial forecasting. To address this, we propose and theoretically justify the application of the so-called supervised scaled principal component analysis (SsPCA) in the context of mixed-data sampling. The SsPCA integrates supervised data weighting to shrink noise components and selectively exploit relevant predictors, enhancing weak factor identification and thereby improving predictive accuracy. Simulation results indicate that SsPCA outperforms other PCA-based or supervised methods, particularly when weak factors are prevalent. In addition, applying mixed-frequency machine learning techniques such as boosting to the cleaner factors extracted by SsPCA yields further gains in predictive performance. Finally, an extensive empirical application to U.S. macro-financial forecasting provides evidence that SsPCA identifies economically meaningful predictors and improves forecasts of GDP, inflation, unemployment, asset prices, and financial market volatility.

Forecasting Economic Time Series in Presence of Weak Factors: Multiple Supervised Learning-Based Approach, with Ulrich Hounyo, 2025, International Journal of Forecasting, Forthcoming

Inference in Supervised Group Factor Models: Applications to Macro-Financial Panel Data

Wild Forests for Causal Inference in Asset Pricing under Heteroskedasticity, with Ulrich Hounyo