Surprise and Uncertainty Indexes: Real-Time Aggregation of Real-Activity Macro Surprises

Abstract

I construct two real-time, real activity indexes: (i) a surprise index that summarizes recent economic data surprises and measures optimism/pessimism about the state of the economy, and (ii) an uncertainty index that measures uncertainty related to the state of the economy. The indexes, on a given day, are weighted averages of the surprises or squared surprises from a set of macro releases, where the weights depend on the contribution of the associated real activity indicator to a business condition indexà la Aruoba, Diebold, and Scotti (2009). I construct indexes for the United States, Euro Area, the United Kingdom, Canada, Japan. I show that the surprise index preserves the properties of the underlying series in affecting asset prices, with the advantage of being a parsimonious summary measure of real-activity surprises. For the United States, I present the real-activity uncertainty index in relation to other proxies commonly used to measure uncertainty and compare their macroeconomic impact. I find evidence that when uncertainty is strictly related to real activity it has a potentially milder impact on economic activity than when it also relates to the financial sector.

Measuring Exchange Rate, Price, and Output Dynamics at the Effective Lower Bound

“…Using the measure of global real activity uncertainty by Scotti (2016) shows that these results are not a mere artefact of the short sample at the ELB (see the online Appendix S1). During normal times, this shock has little impact on the exchange rate and inflation.…”

“…However, we use weights reflecting the share of Swiss exports to the corresponding regions to derive measures that are more relevant to the Swiss economy. In the third specification, we use the monthly average of the global real economic uncertainty measure by Scotti (2016). 27 In addition to the recursive scheme, we use the sign restriction approach by Uhlig (2005) to identify a risk premium shock.…”

The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions

“…(b) the method of Mumtaz and Zanetti (2013), Jurado, Ludvigson, and Ng (2015), Theodoridis (2017, 2018), and Carriero, Clark, and Marcellino (2017) involves recovering measures of uncertainty from stochastic volatility in the error structure of estimated structural VAR models; and (c) finally, the approach by Rossi andSekhposyan (2015, 2017), and Scotti (2016), where these authors have developed measures of uncertainty based on the dispersion of forecasts of key macroeconomic variables produced by the professional forecasters. While there exists no clear-cut consensus in terms of which approach to use in constructing measures of uncertainty, the news-based measures of uncertainty, as developed by Baker et al (2016), seem to have gained tremendous popularity in various applications in macroeconomics and finance.…”

What are the macroeconomic effects of high‐frequency uncertainty shocks?

“…Moreover, uncertainty can also be measured from the disagreement among forecasters on selected macroeconomic variables (see, e.g., Bachmann, Elstner, & Sims, 2013). However, those measures are not available at a monthly frequency or have a too short sample size (e.g., as in Scotti, 2016) and are therefore not considered in our analysis.…”