Bayesian nonparametric inference in bank business models with transient and persistent cost inefficiency
提出一种贝叶斯非参数方法，将银行成本无效率分解为持久和暂时两部分，并基于无限混合模型动态识别银行商业模式，应用于欧洲银行数据发现四类模式。
This paper introduces a novel econometric framework for identifying and modeling bank business models (BBMs), which dynamically evolve in response to changing financial and economic conditions. Building on the stochastic frontier literature, we extend the traditional cost-efficiency models by decomposing inefficiency into persistent and transient components. We propose a Bayesian nonparametric approach that adapts to the data through an infinite mixture model with predictor-dependent clustering, enabling a flexible classification of banks into distinct business models. Our method, based on the Logit Stick-Breaking Process (LSBP), provides a data-driven way to capture the heterogeneity in bank strategies, allowing for dynamic transitions between business models over time. This model offers a significant advancement over existing parametric and kernel-based approaches by combining the scalability of nonparametric methods with efficient computational routines. We apply the model to a dataset of European banks and identify four distinct business model clusters, providing novel insights into the evolution of bank performance and efficiency. Our findings contribute to the growing literature on the identification and measurement of bank business models, offering valuable implications for policy and regulatory frameworks.