Endogenous Uncertainty

Abstract

We show that macroeconomic uncertainty can be considered as exogenous when assessing its effects on the U.S. economy. Instead, fi nancial uncertainty can at least in part arise as an endogenous response to some macroeconomic developments, and overlooking this channel leads to distortions in the estimated effects of fi nancial uncertainty shocks on the economy. We obtain these empirical fi ndings with an econometric model that simultaneously allows for contemporaneous effects of both uncertainty shocks on economic variables and of economic shocks on uncertainty. While the traditional econometric approaches do not allow us to simultaneously identify both of these transmission channels, we achieve identifi cation by exploiting the heteroskedasticity of macroeconomic data. Methodologically, we develop a structural VAR with time-varying volatility in which one of the variables (the uncertainty measure) impacts both the mean and the variance of the other variables. We provide conditional posterior distributions for this model, which is a substantial extension of the popular leverage model of Jacquier, Polson, and Rossi (2004), and provide an MCMC algorithm for estimation.

Exogenous uncertainty and the identification of structural vector autoregressions with external instruments

“…The empirical results discussed in the previous section allow us to make direct contact with Angelini et al (), Carriero et al (), and Ludvigson et al ().…”

Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts

“…see Schorfheide and Song (2015), Kim and Swanson (2018)) or, at best, for the subperiods that either end or even start just before the Great Recession (e.g. see Carriero et al (2015), Foroni et al (2015), Giannone et al (2016)). In doing so, these studies are likely to conceal differences in forecasting performance across recessions and expansions of competing models, potentially leading to the erroneous conclusions regarding the ranking of these models based on their relative predictive ability.…”

“…In this paper we investigate whether the conclusions reached in Chauvet and Potter (2013) for forecasting models involving variables sampled at a single (quarterly) frequency can be generalised to models dealing with economic variables sampled at heterogeneous frequencies. Since the seminal work of Ghysels et al (2004) and Ghysels et al (2007), mixed-frequency models have gained enourmous popularity among the forecasting community, with many different modifications proposed to the original model specifications (Siliverstovs (2017), Carriero et al (2015), Foroni et al (2015), Schorfheide and Song (2015), Guérin and Marcellino (2013), Marcellino and Schumacher (2010), inter alia); see Foroni and Marcellino (2013) for an overview. However, to the best of our knowledge the question of the comparative predictive ability of mixed-frequency models during economic booms and busts has not yet been addressed in a systematic way.…”

“…For this purpose, we utilise the Bayesian mixed-frequency model with stochastic volatility proposed in Carriero et al (2015) for forecasting US GDP growth using the 12 most closely monitored monthly economic/financial indicators. Our choice of this study for emphasising the importance of verifying asymmetries in the predictive ability of competing models across the business cycle phases is not purely incidental.…”

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In this paper we reassess the forecasting performance of the Bayesian mixedfrequency model suggested in Carriero et al (2015) in terms of point and density forecasts of the GDP growth rate using US macroeconomic data. Following Chauvet and Potter (2013), we evaluate the forecasting accuracy of the model relative to a univariate AR(2) model separately for expansions and recessions, as defined by the NBER business cycle chronology, rather than relying on a comparison of forecast accuracy over the whole forecast sample spanning from the first quarter of 1985 to the third quarter of 2011.

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