2021
DOI: 10.1016/j.strueco.2021.05.005
|View full text |Cite
|
Sign up to set email alerts
|

Endogenous fluctuations in demand and distribution: An empirical investigation

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 9 publications
(4 citation statements)
references
References 38 publications
0
4
0
Order By: Relevance
“…500 and 504, respectively), and moreover that a higher Gini coefficient strengthens both effects. Utilizing machine learning methods, Barrales and von Arnim (2020) investigate whether U.S. post‐war data support the existence of an endogenous distributive cycle in activity and labor share, and conclude with a qualified yes.…”
Section: Evidencementioning
confidence: 99%
“…500 and 504, respectively), and moreover that a higher Gini coefficient strengthens both effects. Utilizing machine learning methods, Barrales and von Arnim (2020) investigate whether U.S. post‐war data support the existence of an endogenous distributive cycle in activity and labor share, and conclude with a qualified yes.…”
Section: Evidencementioning
confidence: 99%
“…Accordingly, we set the cut off periodicity at a conservative 64 quarter, that is, any fluctuation beyond that is considered as trend. While this extends beyond standard definitions of the business cycle, recent literature has highlighted the fact that an important frequency peak for economic activity and labor share occurs around 40 quarters (Barrales‐Ruiz and von Arnim, 2021). Further, Barrales‐Ruiz et al.…”
Section: Results For the Post‐war Us Economymentioning
confidence: 96%
“…Third, the method applied here places emphasis on the cyclical nature of the interaction between key macroeconomic variables. With this, we contribute to a resurgence in interest in this area (Barrales‐Ruiz & von Arnim, 2017, 2021; Beaudry et al., 2020; Strohsal et al., 2019). Transformation of vector autoregression results into the frequency domain facilitates the assessment and visualization of causality across the relevant frequency range—from the very short run to the medium run of about 16 years.…”
Section: Discussionmentioning
confidence: 94%
See 1 more Smart Citation