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Chen Gao

Personal Details

First Name:Chen
Middle Name:
Last Name:Gao
Suffix:
RePEc Short-ID:pga969
[This author has chosen not to make the email address public]

Affiliation

Department of Agricultural Economics
Texas A&M University

College Station, Texas (United States)
http://agecon.tamu.edu/
RePEc:edi:datamus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. gao, chen & leatham, david, 2017. "Monetary policy, Farm sector income and Farm Household Well-being --a VECM Analysis," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252815, Southern Agricultural Economics Association.
  2. Fei, Chengcheng & Gao, Chen & Hardin, Erin M. & Dharmasena, Senarath, 2016. "Application of Demand Analysis Framework to Understand the Price and Volume Movements of Exchange Traded Funds (ETFs)," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 229798, Southern Agricultural Economics Association.

Articles

  1. Chen Gao & Chengcheng J. Fei & Bruce A. McCarl & David J. Leatham, 2020. "Identifying Vulnerable Households Using Machine Learning," Sustainability, MDPI, vol. 12(15), pages 1-18, July.
  2. Grace Wang & Chen Gao, 2012. "Technical Efficiency and Port Competition: Revisiting the Bohai Economic Rim, China," JRFM, MDPI, vol. 5(1), pages 1-16, December.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

    Sorry, no citations of working papers recorded.

Articles

  1. Chen Gao & Chengcheng J. Fei & Bruce A. McCarl & David J. Leatham, 2020. "Identifying Vulnerable Households Using Machine Learning," Sustainability, MDPI, vol. 12(15), pages 1-18, July.

    Cited by:

    1. Mark Musumba & Naureen Fatema & Shahriar Kibriya, 2021. "Prevention Is Better Than Cure: Machine Learning Approach to Conflict Prediction in Sub-Saharan Africa," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    2. Sayed Alim Samim & Zhiquan Hu & Sebastian Stepien & Sayed Younus Amini & Ramin Rayee & Kunyu Niu & George Mgendi, 2021. "Food Insecurity and Related Factors among Farming Families in Takhar Region, Afghanistan," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
    3. Meerza, Syed Imran Ali & Meerza, Syed Irfan Ali & Ahamed, Afsana, 2021. "Food Insecurity Through Machine Learning Lens: Identifying Vulnerable Households," 2021 Annual Meeting, August 1-3, Austin, Texas 314072, Agricultural and Applied Economics Association.
    4. Hazal Colak Oz & Çiçek Güven & Gonzalo Nápoles, 2023. "School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach," Journal of Computational Social Science, Springer, vol. 6(1), pages 245-287, April.
    5. Adham Alsharkawi & Mohammad Al-Fetyani & Maha Dawas & Heba Saadeh & Musa Alyaman, 2021. "Poverty Classification Using Machine Learning: The Case of Jordan," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
    6. Shuo Ding, 2022. "A Comparative Analysis of Vulnerability to Poverty between Urban and Rural Households in China," Economies, MDPI, vol. 10(10), pages 1-28, October.

  2. Grace Wang & Chen Gao, 2012. "Technical Efficiency and Port Competition: Revisiting the Bohai Economic Rim, China," JRFM, MDPI, vol. 5(1), pages 1-16, December.

    Cited by:

    1. Kammoun, Rabeb & Abdennadher, Chokri, 2022. "Seaport efficiency and competitiveness in European seaports," Transport Policy, Elsevier, vol. 121(C), pages 113-124.
    2. Chia-Nan Wang & Jen-Der Day & Nguyen Thi Kim Lien & Luu Quoc Chien, 2018. "Integrating the Additive Seasonal Model and Super-SBM Model to Compute the Efficiency of Port Logistics Companies in Vietnam," Sustainability, MDPI, vol. 10(8), pages 1-17, August.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-MAC: Macroeconomics (1) 2017-02-05
  2. NEP-MON: Monetary Economics (1) 2017-02-05
  3. NEP-MST: Market Microstructure (1) 2016-04-30

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