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

By Chiara Scotti. 2016

357 citations recorded by the source corpus.

I construct two daily, real-time, real activity indexes for the United States, Euro area, the United Kingdom, Canada, and Japan: (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 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, the real-activity uncertainty index is compared to other proxies commonly used to measure uncertainty to show 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.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/dfcf71ec8e1a30167c4b45d7d5d9332f97bfa882

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

Citations in the corpus

Economic policy uncertainty and real output: evidence from the G7 countries (2018, neutral): “Also, Scotti (2016) constructed both a surprise index (that summarizes recent economic data surprises and measured deviations from consensus expectations) and an uncertainty index (that measures how uncertain economic agents are about the current real activity conditions) for the United States, the…”
Surveying business uncertainty (2019, background): “Scotti (2016) uses surprises in economic data releases to proxy for uncertainty.”
Dynamic Connectedness of Uncertainty across Developed Economies: A Time-Varying Approach (2018, methodology): “We compile a dataset of daily macroeconomic uncertainty indices from Scotti (2016) for the U.”
Influential factors in crude oil price forecasting (2017, background): “The ISM Manufacturing Index is widely viewed as an indicator of the business cycle (Scotti, 2013).”
Macroeconomic Uncertainty Through the Lens of Professional Forecasters (2019, positive): “Professional forecasters base their forecasts on various econometric models, leading indicators and surveys as well as a large number of available macroeconomic and financial data, encompassing information from a wide range of sources (see Zarnowitz and Braun 1993). Hence, survey forecasts provide a”
Monetary policy uncertainty (2017, background): “See also Stock and Watson (2012) and Rogers, Scotti, and Wright (2016) (RSW).”
Policy uncertainty, investment, and the cost of capital (2018, neutral): “Bachmann et al. (2013), Scotti (2016), Bloom (2014), and Jurado et al. (2015) document that macroeconomic forecasts tend to be noisier, forecasters are less confident about their forecasts, and their forecasts are more optimistic than actual outcomes during recession periods.”
The pandemic and economic policy uncertainty (2020, neutral): “Scotti (2016) also proposed a real-time activity index based on two components: unexpected events, and the government and economy.”