Financial and Macroeconomic Data Through the Lens of a Nonlinear Dynamic Factor Model

Pablo Guerron, Alexey Khazanov and Molin Zhong

No 2023-027, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Through the lens of a nonlinear dynamic factor model, we study the role of exogenous shocks and internal propagation forces in driving the fluctuations of macroeconomic and financial data. The proposed model 1) allows for nonlinear dynamics in the state and measurement equations; 2) can generate asymmetric, state-dependent, and size-dependent responses of observables to shocks; and 3) can produce time-varying volatility and asymmetric tail risks in predictive distributions. We find evidence in favor of nonlinear dynamics in two important U.S. applications. The first uses interest rate data to extract a factor allowing for an effective lower bound and nonlinear dynamics. Our estimated factor coheres well with the historical narrative of monetary policy. We find that allowing for an effective lower bound constraint is crucial. The second recovers a credit cycle. The nonlinear component of the factor boosts credit growth in boom times while hinders its recovery post-crisis. Shocks in a credit crunch period are more amplified and persist for longer compared with shocks during a credit boom.

Keywords: Interest rates; Effective lower bound; Credit cycle; Asymmetric dynamics; Predictive distributions; Tail risk (search for similar items in EconPapers)
JEL-codes: C51 E43 E51 (search for similar items in EconPapers)
Pages: 57 p.
Date: 2023-05-05
New Economics Papers: this item is included in nep-ban, nep-ets and nep-fdg
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2023-27

DOI: 10.17016/FEDS.2023.027

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