The application of multiple-output quantile regression to the US financial cycle
用多输出分位数回归分析美国房价增长与信贷增长的联合分布尾部，揭示系统性风险的关键指标，并评估货币政策冲击的影响，为政策制定者提供预警工具。
The paper demonstrates the benefits of multiple-output quantile regression for macroeconomic analysis. The domestic financial cycle, which is characterized by the co-movement of credit and property prices, is a natural subject of such methodology. More precisely, I examine the tails of the joint distribution of US house price growth and household credit growth since the late 1970s to shed some light on the evolution of systemic risk and its links to various economic and financial factors. The analysis finds that the crucial indicators include the banking sector’s exposure to household credit, household leverage, house price misalignment, and financial market volatility. This contrasts with the negligible role of real-economy factors. In addition, the estimated effect of monetary policy shocks on systemic risk suggests rejecting the “leaning against the wind” strategy. Finally, it is shown that the multiple-output quantile regression framework is a useful tool for forecasting and tracking systemic risk over time. The sustainable growth of house prices and credit can be distinguished from their growth accompanied by the rise in systemic risk to guide policymakers on an appropriate response.