
handle: 11250/2997750 , 11250/2444266 , 11250/2495586 , 10419/210115
AbstractUncertainty is acknowledged to be a source of economic fluctuations. But, does the type of uncertainty matter for the economy's response to an uncertainty shock? This article offers a novel identification strategy to disentangle different types of uncertainty. It uses machine learning techniques to classify different types of news instead of specifying a set of keywords. The article finds that, depending on its source, the effects of uncertainty on a macroeconomic variable may differ. I find that both good (expansionary effect) and bad (contractionary effect) types of uncertainty exist.
VDP::Samfunnsvitenskap: 200::Økonomi: 210::Samfunnsøkonomi: 212, ddc:330, Uncertainty, JEL: D80, Business cycles, Newspaper, JEL: E66, JEL: E32, machine learning, business cycles, Topic model, Machine learning, D80, E66, newspaper, topic model, uncertainty, E32
VDP::Samfunnsvitenskap: 200::Økonomi: 210::Samfunnsøkonomi: 212, ddc:330, Uncertainty, JEL: D80, Business cycles, Newspaper, JEL: E66, JEL: E32, machine learning, business cycles, Topic model, Machine learning, D80, E66, newspaper, topic model, uncertainty, E32
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 33 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
