ORCID

0009-0004-1782-3000

Date of Award

Fall 2026

Language

English

Embargo Period

9-4-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Economics

Program

Economics

First Advisor

Ulrich Hounyo

Committee Members

Kajal Lahiri, Zhongwen Liang

Keywords

Macroeconomic Forecasting, Financial Forecasting, Weak Factors, Supervised Learning, Principal Component Analysis, Big Data

Subject Categories

Econometrics | Finance | Macroeconomics

Abstract

The first chapter proposes an innovative supervised learning technique for dimension reduction and forecasting financial and macroeconomic time series in the presence of weak factors that can undermine the effectiveness of traditional Principal Component Analysis (PCA). This approach employs double or multiple supervised learning procedures and is termed ``Supervised Scaled Principal Component Analysis'' (SsPCA). Initially, each predictor is scaled using its predictive slope on the target forecast variable, giving more weight to those with stronger predictive abilities and less to those with weaker ones. Subsequently, utilizing the scaled predictors, we intelligently identify the most informative subset for prediction. This involves iterative steps of supervised selection, factor extraction through PCA, and projection. The integration of a pre-selection step, aimed at choosing ``targeted predictors'' prior to executing the SsPCA procedure, especially using soft thresholding of supervised learning methods like elastic net, significantly enhances the predictive capacity of SsPCA. Additionally, incorporating the possibility of non-linear relationships between predictors and factors often leads to supplementary improvements. Through extensive Monte Carlo simulation exercises, we find that our proposed SsPCA procedure consistently outperforms standard PCA. Real-world examples involving macroeconomic and financial asset pricing forecasting further suggest that SsPCA generally exhibits superior performance.

The second chapter studies factor-MIDAS regressions, which forecast a low-frequency target by extracting common factors from a large panel of high-frequency predictors via principal component analysis (PCA). While PCA mitigates the curse of dimensionality, it relies on factor pervasiveness, an assumption often violated when factors are weak, as is common in macro-financial forecasting. We propose SsPCA-MIDAS, which integrates supervised scaled PCA (SsPCA) into the mixed-data sampling framework. We establish consistency and asymptotic normality under weak factors, permitting inference on the prediction target. Simulations show that SsPCA-MIDAS outperforms competing PCA-based and supervised methods, especially when weak factors are prevalent. Applying machine-learning techniques such as boosting to the cleaner factors it extracts yields further gains. An extensive application to U.S. macro-financial forecasting shows that SsPCA-MIDAS selects economically meaningful predictors and improves forecasts of GDP, inflation, unemployment, asset prices, and volatility.

License

This work is licensed under the University at Albany Standard Author Agreement.

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