Cross-sectional Aggregation
Cross-Sectional Aggregation is a methodological approach in aggregate demand forecasting that combines data from multiple entities or categories at a single point in time to improve forecast accuracy by leveraging interunit relationships and patterns across the cross-section.
30
Publications
3.3K
Citations
53
Authors
44
Institutions
Publications per year
2017–2026
5
Parents
30
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Sanjiv Ranjan Das, Mike Y. Chen · Management Science · 2007 · 1.4K citations
Business, Data Science, Finance +17
Nina van Loon, Anne Mette Kjeldsen, Lotte Bøgh Andersen et al. · Review of Public Personnel Administration · 2016 · 112 citations · Full text
1–5 of 30
53
3
h-index
2
h-index
2
h-index
2
h-index
1
h-index
1–5 of 53
44
Massachusetts Institute of Technology
Cambridge, United States
4 h-index
4 publications
101 citations
University of Cambridge
Cambridge, United Kingdom
3 h-index
3 publications
151 citations
University of Southern California
Los Angeles, United States
2 h-index
2 publications
137 citations

Aarhus University
Aarhus, Denmark
2 h-index
5 publications
404 citations
Federal Reserve Board of Governors
Washington D.C., United States
2 h-index
2 publications
41 citations
Federal Reserve
Washington D.C., United States
2 h-index
2 publications
41 citations

National Bureau of Economic Research
Cambridge, United States
2 h-index
2 publications
121 citations
Health Decision Technologies (United States)
Oakland, United States
1 h-index
1 publications
106 citations
1–8 of 44
5 publications · 212 citations
5
h-index
2 publications · 93 citations
2
h-index
Repository · 2 publications · 24 citations
2
h-index
2 publications · 107 citations
2
h-index
1 publications · 31 citations
1
h-index
1–5