Keehyun Lee Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
faculty
Research Areas
Biography and Research Information
OverviewAI-generated summary
Keehyun Lee's research investigates various aspects of household behavior and policy impacts. Recent publications include studies on machine learning applications for demand prediction, the effects of immigration policy changes on participation in nutrition programs, and the influence of market information releases on grain futures volatility. Lee has also examined how household headship and gender influence diet quality within the Supplemental Nutrition Assistance Program (SNAP) and analyzed habitual food expenditure patterns by store type. Collaborations include work with Eunchun Park, Andrew M. McKenzie, Andrew D.M. Anderson, and Andrew E. Anderson, resulting in shared publications. Lee's work demonstrates a focus on understanding economic and behavioral factors influencing household decisions and market dynamics.
Metrics
- h-index: 6
- Publications: 18
- Citations: 211
Selected Publications
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Private Forecasts and Market Dynamic: The Influence of Private Forecasts on Commodity Futures Markets (2024)
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Exploring calendar effects: the impact of WASDE releases on grain futures market volatility (2024)
Collaboration Network
Top Collaborators
- Machine learning applications in household-level demand prediction
- Do Household Headship and Gender Affect Diet Quality under the Supplemental Nutrition Assistance Program (SNAP)?
- Habitual behavior of household food expenditure by store type in the United States
- The effect of immigration policy regime change on state-level participation rates of the special supplemental nutrition program for women, infants, and children in the United States
- Exploring calendar effects: the impact of WASDE releases on grain futures market volatility
- Private Forecasts and Market Dynamic: The Influence of Private Forecasts on Commodity Futures Markets
- Machine learning applications in household-level demand prediction
- Machine learning applications in household-level demand prediction
- Private Forecasts and Market Dynamic: The Influence of Private Forecasts on Commodity Futures Markets
- Private Forecasts and Market Dynamic: The Influence of Private Forecasts on Commodity Futures Markets
- Private Forecasts and Market Dynamic: The Influence of Private Forecasts on Commodity Futures Markets
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