Uncertainty Shocks and Unemployment Dynamics in U.S. Recessions

Caggiano, Giovanni; Castelnuovo, Efrem; Groshenny, Nicolas

doi:10.2139/ssrn.2437769

Cited by 153 publications

(268 citation statements)

References 31 publications

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“…Sources: Author's calculations; Factiva 6 I use the monthly average of the absolute value of daily percentage changes because it is most strongly correlated with the option-implied measure over the period for which data exist for both measures (correlation of 0.85). This is in slight contrast to Bloom (2009) andCaggiano et al (2014), who use the within-month standard deviation of daily percentage changes. However, there is little difference between the two series.…”

Section: Forecaster Disagreementmentioning

confidence: 94%

“…Based on a similar aggregate-level VAR framework, Bloom (2009) finds that uncertainty shocks lead to a 1 per cent decrease in industrial production and about a 0.7 per cent decrease in employment. In a similar vein, Caggiano et al (2014) use a smooth transition VAR, which allows them to differentiate between recessions and expansions, and find that an uncertainty shock raises the unemployment rate by about 0.17 percentage points 4 quarters after an uncertainty shock. During a recession, the effect is larger, about 0.36 percentage points.…”

Section: (Iii) Comparison To Other Literaturementioning

confidence: 99%

“…Stock market volatility is a commonly used proxy for uncertainty because it is available in real time and is reasonably comparable across countries. For example: Caggiano et al (2014) use it to assess the effects of uncertainty on the US economy; Bekaert et al (2013) decompose it into a risk aversion component and an uncertainty component to test how monetary policy affects both; and Baker and Bloom (2013) use natural disasters and stock market volatility to assess the effects of uncertainty.…”

Section: Finance-based Measuresmentioning

confidence: 99%

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Measuring Economic Uncertainty and Its Effects

Moore

2017

Economic Record

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I construct a monthly index of economic uncertainty for Australia. Economic uncertainty rose to historically high levels during the financial crisis and remained elevated until late 2013. The index is: higher around recessions, elections, monetary policy surprises and some major events; tends to increase faster than it decreases; and is driven by both domestic and foreign factors. I use the index to assess how uncertainty affects the Australian economy. Consistent with the ‘real options’ channel of uncertainty, I find that it reduces investment and employment growth. Similarly, uncertainty raises the household saving ratio, consistent with the ‘precautionary savings’ channel.

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Section: Forecaster Disagreementmentioning

confidence: 94%

Section: (Iii) Comparison To Other Literaturementioning

confidence: 99%

Section: Finance-based Measuresmentioning

confidence: 99%

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Measuring Economic Uncertainty and Its Effects

Moore

2017

Economic Record

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“…Several alternative measures have been proposed to investigate the dynamics of uncertainty in the macroeconomy and its relation to economic activity. A number of studies use the optionbased CBOE implied volatility index (VXO) to measure uncertainty perceived on financial markets (see, for instance, Basu and Bundick 2012;Bloom 2009;Caggiano, Castelnuovo and Groshenny 2014). Figure 7 compares the VXO and the indicator of business cycle uncertainty.…”

Section: Figurementioning

confidence: 99%

“…Nonlinear effects of uncertainty are also considered by Alessandri and Mumtaz (2014) and Caggiano, Castelnuovo, and Nodari (2014), among others. Caggiano, Castelnuovo, and Groshenny (2014) and Nodari (2014) conclude from smooth transition VAR models that uncertainty has a larger effect in recessions on unemployment and credit spreads, respectively. 26.…”

Section: Macroeconomic Uncertainty and Real Activitymentioning

confidence: 99%

Dimensions of Macroeconomic Uncertainty: A Common Factor Analysis

Henzel

Rengel

2016

Economic Inquiry

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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. Uncertainty about the future course of the economy is a possible driver of aggregate fluctuations. To identify the different dimensions of uncertainty in the macroeconomy we construct a large dataset covering all types of economic uncertainty. We then identify two fundamental factors which account for the common dynamics in this dataset. These factors are interpreted as macroeconomic uncertainty. The first factor mainly captures business cycle uncertainty while the second factor is identified as oil and commodity price uncertainty. While both types of uncertainty foreshadow a decline in output, surprise increases in oil and commodity price uncertainty appear to be more important for fluctuations in real activity. Terms of use: Documents in EconStor mayJEL-Code: C320, C380, E320.

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Forecasting US GNP growth: The role of uncertainty

Segnon

Gupta

Bekiros

et al. 2018

Journal of Forecasting

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A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.

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Uncertainty Shocks and Unemployment Dynamics in U.S. Recessions

Cited by 153 publications

References 31 publications

Measuring Economic Uncertainty and Its Effects

Measuring Economic Uncertainty and Its Effects

Dimensions of Macroeconomic Uncertainty: A Common Factor Analysis

Forecasting US GNP growth: The role of uncertainty

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