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Probabilistic Targeted Factor Analysis

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  • Miguel C. Herculano
  • Santiago Montoya-Bland'on

Abstract

We develop a probabilistic variant of Partial Least Squares (PLS) we call Probabilistic Targeted Factor Analysis (PTFA), which can be used to extract common factors in predictors that are useful to predict a set of predetermined target variables. Along with the technique, we provide an efficient expectation-maximization (EM) algorithm to learn the parameters and forecast the targets of interest. We develop a number of extensions to missing-at-random data, stochastic volatility, and mixed-frequency data for real-time forecasting. In a simulation exercise, we show that PTFA outperforms PLS at recovering the common underlying factors affecting both features and target variables delivering better in-sample fit, and providing valid forecasts under contamination such as measurement error or outliers. Finally, we provide two applications in Economics and Finance where PTFA performs competitively compared with PLS and Principal Component Analysis (PCA) at out-of-sample forecasting.

Suggested Citation

  • Miguel C. Herculano & Santiago Montoya-Bland'on, 2024. "Probabilistic Targeted Factor Analysis," Papers 2412.06688, arXiv.org.
  • Handle: RePEc:arx:papers:2412.06688
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    References listed on IDEAS

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