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Citation link: https://media.suub.uni-bremen.de/handle/elib/8609

Publisher DOI: https://doi.org/10.1007/s00291-024-00771-1
 
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Recurrent double-conditional factor model


Authors: Fieberg, Christian  
Liedtke, Gerrit  
Poddig, Thorsten 
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.
Issue Date: 2024
Publisher: Springer
Journal/Edited collection: OR Spectrum 
Type: Artikel/Aufsatz
ISSN: 0171-6468
Institution: Hochschule Bremen 
Faculty: Hochschule Bremen - Fakultät 1: Wirtschaftswissenschaften - School of International Business (SiB) 
Appears in Collections:Bibliographie HS Bremen

  

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