Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?

Ludvigson, Sydney C.; Ma, Stefan; Ng, Serena

doi:10.3386/w21803

Cited by 327 publications

(667 citation statements)

References 29 publications

(29 reference statements)

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“…In an extended specication, we consider a VAR with eight variables, similar to Bloom (2009). In a robustness exercise, we use the nancial uncertainty measure estimated by Ludvigson, Ma, and Ng (2015), instead of VIX.…”

Section: Us Uncertainty Shockmentioning

confidence: 99%

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Global Spillover Effects of US Uncertainty

Bhattarai¹,

Chatterjee²,

Park³

2017

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We study spillover effects of US uncertainty fluctuations using panel data from fifteen emerging market economies (EMEs). A US uncertainty shock negatively affects EME stock prices and exchange rates, raises EME country spreads, and leads to capital outflows from them. Moreover, it decreases EME output, while increasing their consumer prices and net exports. The negative effects on output, exchange rates, and stock prices are weaker, but the effects on capital and trade flows stronger, for South American countries compared to other EMEs. We present a model of a small open economy that faces an external shock to interpret our findings.JEL codes: C33, E44, E52, E58, F32, F41

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Section: Us Uncertainty Shockmentioning

confidence: 99%

“…In a robustness exercise, we use the US nancial uncertainty measure estimated by Ludvigson, Ma, and Ng (2015).…”

Section: The Panel Var Includes Anmentioning

confidence: 99%

Global Spillover Effects of US Uncertainty

Bhattarai¹,

Chatterjee²,

Park³

2017

gwp

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“…Table 1 shows the cor-relation between the U.S. GTU index and a variety of di¤erent proxies for uncertainty proposed in the literature and available at monthly frequency. We consider the VXO used by Bloom (2009); the EPU index constructed by Baker, Bloom, and Davis (2016); the macroeconomic uncertainty index proposed by Jurado, Ludvigson, and Ng (2015); the …nancial uncertainty index constructed by Ludvigson, Ma, and Ng (2016); the subjective interest rate uncertainty proposed by Istre… and Mouabbi (2017); the categorical measure of monetary policy-related uncertainty produced by Baker, Bloom, and Davis (2016); the real-time, real activity related uncertainty index constructed by Scotti (2016); and the real-time measure of uncertainty based on the distribution of the forecast errors of real GDP constructed by Rossi and Sekhposyan (2015). Scotti's (2016) index is available at daily frequencies.…”

Section: Gtu Indexmentioning

confidence: 99%

Google It Up! A Google Trends-based Uncertainty index for the United States and Australia

2017

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We develop uncertainty indices for the United States and Australia based on freely accessible, real time Google Trends data. Our Google Trends Uncertainty (GTU) indices are found to be positively correlated to a variety of alternative proxies for uncertainty available for these two countries. VAR investigations document an economically and statistically signi…cant contribution to unemployment dynamics by GTU shocks in the United States. In contrast, the contribution of GTU shocks to unemployment dynamics in Australia is found to be much milder and substantially lower than that of monetary policy shocks.

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“…31 While the measure proposed by Rossi and Sekhposyan (2015) is available for a large number of countries, the time series dimension is unbalanced and often not long enough for our purposes. Finally, model based measures, such as those in Jurado et al (2015) and Ludvigson et al (2015) could in principle be computed for all countries in our sample, but the data requirements to construct such measures for many countries over a sufficiently long time period are prohibitive.…”

Section: Volatility Measurementmentioning

confidence: 99%

“…The first strand acknowledges that uncertainty may be endogenous and could be driven by the business cycle (See, for instance, Ludvigson et al (2015), Clark et al (2016), and Berger et al (2017)). 8 A key difference relative to these contributions, is that our identification assumptions apply to a cross-section of countries, as opposed to a single country considered in isolation from the rest of the world, or the global economy analyzed as a single, closed economy.…”

Section: Figure 2 Average Pair-wise Correlations Of Volatility and Grmentioning

confidence: 99%

Uncertainty and Economic Activity: A Multi-Country Perspective

2018

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Measures of economic uncertainty are countercyclical, but economic theory does not provide definite guidance on the direction of causation between uncertainty and the business cycle. This paper proposes a new multi-country approach to the analysis of the interaction between uncertainty and economic activity, without a priori restricting the direction of causality. We develop a multi-country version of the Lucas tree model with time-varying volatility and show that in addition to common technology shocks that affect output growth, higher-order moments of technology shocks are also required to explain the cross country variations of realized volatility. Using this theoretical insight, two common factors, a 'real' and a 'financial' one, are identified in the empirical analysis assuming different patterns of cross-country correlations of country-specific innovations to real GDP growth and realized stock market volatility. We then quantify the absolute and the relative importance of the common factor shocks as well as country-specific volatility and GDP growth shocks. The paper highlights three main empirical findings. First, it is shown that most of the unconditional correlation between volatility and growth can be accounted for by the real common factor, which is proportional to world growth in our empirical model and linked to the risk-free rate. Second, the share of volatility forecast error variance explained by the real common factor and by country-specific growth shocks amounts to less than 5 percent. Third, shocks to the common financial factor explain about 10 percent of the growth forecast error variance, but when such shocks occur, their negative impact on growth is large and persistent. In contrast, country-specific volatility shocks account for less than 1-2 percent of the growth forecast error variance. Terms of use: Documents inJEL-Codes: E440, F440, G150.

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Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?

Cited by 327 publications

References 29 publications

Global Spillover Effects of US Uncertainty

Global Spillover Effects of US Uncertainty

Google It Up! A Google Trends-based Uncertainty index for the United States and Australia

Uncertainty and Economic Activity: A Multi-Country Perspective

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