Recurrent double-conditional factor model

Christian Fieberg (), Gerrit Liedtke () and Thorsten Poddig ()
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Christian Fieberg: HSB Hochschule Bremen - City University of Applied Sciences
Gerrit Liedtke: University of Bremen
Thorsten Poddig: University of Bremen

OR Spectrum: Quantitative Approaches in Management, 2025, vol. 47, issue 1, No 7, 205-254

Abstract: Abstract In economic applications, the behavior of objects (e.g., individuals, firms, or households) is often modeled as a function of microeconomic and/or macroeconomic conditions. While macroeconomic conditions are common to all objects and change only over time, microeconomic conditions are object-specific and thus vary both among objects and through time. The simultaneous modeling of microeconomic and macroeconomic conditions has proven to be extremely difficult for these applications due to the mismatch of dimensions, potential interactions, and the high number of parameters to estimate. By marrying recurrent neural networks with conditional factor models, we propose a new white-box machine learning method, the recurrent double-conditional factor model (RDCFM), which allows for the modeling of the simultaneous and combined influence of micro- and macroeconomic conditions while being parsimoniously parameterized. Due to the low degree of parameterization, the RDCFM generalizes well and estimation remains feasible even if the time-series and the cross-section are large. We demonstrate the suitability of our method using an application from the financial economics literature.

Keywords: Machine learning; Recurrent neural network; Factor model; Panel data analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00291-024-00771-1

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