The Term Structure of Growth-at-Risk

Adrian, Tobias; Grinberg, Federico; Liang, Nellie; Malik, Sheheryar

doi:10.1257/mac.20180428

Cited by 35 publications

(38 citation statements)

References 39 publications

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“…Two recent events, the global financial crisis and the COVID‐19 pandemic, have increased interest in tail risks in macroeconomic outcomes. A fast‐growing literature has focused on the risks of significant declines in GDP, with quantile regression being the main method used to estimate tail risks (see, e.g., Adrian et al., 2019, 2022; Cook and Doh, 2019; Delle Monache et al., 2020; De Nicolò and Lucchetta, 2017; Ferrara et al., 2022; Giglio et al., 2016; González‐Rivera et al., 2019; Mitchell et al., 2022; Plagborg‐Møller et al., 2020; Reichlin et al., 2020). Some studies focused on tail risks to unemployment (e.g., Galbraith and van Norden, 2019; Kiley, 2022) or inflation (e.g., Ghysels et al., 2018) or deal with forecasting the complete distribution of several macroeconomic aggregates (see Manzan, 2015; Korobilis, 2017; Manzan and Zerom, 2013, 2015).…”

Section: Introductionmentioning

confidence: 99%

Tail Forecasting With Multivariate Bayesian Additive Regression Trees

Clark

Huber

Koop

et al. 2023

Int Economic Review

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We develop novel multivariate time series models using Bayesian additive regression trees that posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a novel nonparametric specification. Estimation is carried out using scalable Markov chain Monte Carlo estimation algorithms for each specification. We evaluate the real-time density and tail forecasting performance of the various models for a set of US macroeconomic and financial indicators. Our results suggest that using nonparametric models generally leads to improved forecast accuracy. In particular, when interest centers on the tails of the posterior predictive, flexible models improve upon standard VAR models with SV. Another key finding is that if we allow for nonlinearities in the conditional mean, allowing for heteroskedasticity becomes less important. A scenario analysis reveals highly nonlinear relations between the predictive distribution and financial conditions.

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Section: Introductionmentioning

confidence: 99%

Tail Forecasting With Multivariate Bayesian Additive Regression Trees

Clark

Huber

Koop

et al. 2023

Int Economic Review

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“…Adrian, et al (2022) coined the term "growth at risk" for GDP growth forecasts, and Denicolo and Lucchetta (2017) coined similar terms for industrial production and employment.7 SeeAmburgey and McCracken (2022) for a comparison of results from the small GDP growth model ofAdrian, Boyarchenko, and Giannone (2019) to a quantile regression including just lagged GDP growth.…”

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confidence: 99%

Specification Choices in Quantile Regression for Empirical Macroeconomics

Carriero

Clark

Marcellino

2022

Working Paper (Federal Reserve Bank of Cleveland)

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Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks to macroeconomic indicators. In this paper we examine various choices in the specification of quantile regressions for macro applications, for example, choices related to how and to what extent to include shrinkage, and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, using for evaluation both quantile scores and quantile-weighted continuous ranked probability scores at a range of quantiles spanning from the left to right tail. We find that shrinkage is generally helpful to tail forecast accuracy, with gains that are particularly large for GDP applications featuring large sets of predictors and unemployment and inflation applications, and with gains that increase with the forecast horizon.

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“…According to research by Brandão-Marques et al (2022), the riskiness of credit allocation helps predict shifts in the left tail of the GDP growth distribution and financial stress episodes. Adrian et al (2022) argued that financial conditions significantly affect growth-at-risk and that loose financial conditions have a causal relationship with future downside risk. More noteworthy is that the financial cycles are an essential reference for predicting the risk of economic recession and a prerequisite for dealing with economic fluctuations (see Borio et al, 2019;Li et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

The mean reversion/persistence of financial cycles: Empirical evidence for 24 countries worldwide

Zhang²,

Fan³

et al. 2023

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Research background: The globalization trend has inevitably enhanced the connectivity of global financial markets, making the cyclicality of financial activities and the spread of market imbalances have received widespread attention, especially after the global financial crisis. Purpose of the article: To reduce the negative effects of the contagiousness of the financial cycles, it is necessary to study the persistence of financial cycles and carve out the total connectedness, spillover paths, and sources of risks on a global scale. In addition, understanding the relationship between the financial cycle and economic development is an important way to prevent financial crises. Methods: This paper adopts the nonlinear smoothing transition autoregressive (STAR) model to extract cyclical and phase characteristics of financial cycles based on 24 countries during 1971Q1?2015Q4, covering developed and developing countries, the Americas, Europe, and Asia regions. In addition, the frequency connectedness approach is used to measure the connectedness of financial cycles and the relationship between the global financial cycle and the global economy. Findings & value added: The analysis reveals that aggregate financial cycles persist for 13.3 years for smoothed and 8.7 years for unsmoothed on average. The national financial cycles are asynchronous and exhibit more prolonged expansions and faster contractions. The connectedness of financial cycles is highly correlated with systemic crises and contributes to the persistence and harmfulness of shocks. It is mainly driven by short-term components and exhibits more pronounced interconnectedness within regions than across regions. During the financial crisis, the global financial cycle movements precede and are longer than the business fluctuations. Based on the study, some policy implications are presented. This paper emphasizes the impact of systemic crises on the persistence of financial cycles and their connectedness, which contributes to refining research related to the coping mechanisms of financial crises.

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The Term Structure of Growth-at-Risk

Cited by 35 publications

References 39 publications

Tail Forecasting With Multivariate Bayesian Additive Regression Trees

Tail Forecasting With Multivariate Bayesian Additive Regression Trees

Specification Choices in Quantile Regression for Empirical Macroeconomics

The mean reversion/persistence of financial cycles: Empirical evidence for 24 countries worldwide

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