Financial shocks, such as the one observed during the global financial crisis, exhibit important domestic and international consequences on macroeconomic aggregates (see, e.g., Dovern and van Roye, 2014; Ciccarelli et al., 2016; Prieto et al., 2016; Gerba et al., 2024). Policymakers in central banks and governmental institutions, who aim to smooth business cycles and thus alleviate the negative effects of adverse financial disruptions, need to understand how such shocks impact the economy and propagate internationally to implement policies in a forward-looking manner. The recent literature provides plenty of evidence on the domestic and international effects of US financial shocks (see Balke, 2000; Gilchrist and Zakrajšek, 2012; Cesa-Bianchi and Sokol, 2022). These papers find that financial shocks exert powerful effects on domestic output but also that US-based shocks spill over to foreign economies and trigger declines in international economic activity. Such effects might be subject to time variation (Abbate et al., 2016).

This paper proposes a new Bayesian machine learning model that can be applied to large datasets arising in macroeconomics. Our framework sums over many simple two-component location mixtures. The transition between components is determined by a logistic function that depends on a single threshold variable and two hyperparameters. Each of these individual models only accounts for a minor portion of the variation in the endogenous variables. But many of them are capable of capturing arbitrary nonlinear conditional mean relations. Conjugate priors enable fast and efficient inference. In simulations, we show that our approach produces accurate point and density forecasts. In a real-data exercise, we forecast US macroeconomic aggregates and consider the nonlinear effects of financial shocks in a large-scale nonlinear VAR.

This research article analyses and demonstrates the hidden implications for fairness of seemingly neutral data coupled with powerful technology, such as machine learning (ML), using Open Banking as an example. Open Banking has ignited a revolution in financial services, opening new opportunities for customer acquisition, management, retention, and risk assessment. However, the granularity of transaction data holds potential for harm where unnoticed proxies for sensitive and prohibited characteristics may lead to indirect discrimination. Against this backdrop, we investigate the dimensions of financial vulnerability (FV), a global concern resulting from COVID-19 and rising inflation. Specifically, we look to understand the behavioral elements leading up to FV and its impact on at-risk, disadvantaged groups through the lens of fair interpretation. Using a unique dataset from a UK FinTech lender, we demonstrate the power of fine-grained transaction data while simultaneously cautioning its safe usage. Three ML classifiers are compared in predicting the likelihood of FV, and groups exhibiting different magnitudes and forms of FV are identified via clustering to highlight the effects of feature combination. Our results indicate that engineered features of financial behavior can be predictive of omitted personal information, particularly sensitive or protected characteristics, shedding light on the hidden dangers of Open Banking data. We discuss the implications and conclude fairness via unawareness is ineffective in this new technological environment.