Internet use and technical efficiency of grain production in China: a bias-corrected stochastic frontier model
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
Internet use and technical efficiency of grain production in China: a bias-corrected stochastic frontier model ; volume:10 ; number:1 ; day:5 ; month:10 ; year:2023 ; pages:1-13 ; date:12.2023
Humanities and Social Sciences Communications ; 10, Heft 1 (5.10.2023), 1-13, 12.2023
- Classification
-
Wirtschaft
- Creator
-
Fu, Yangqi
Zhu, Yuchun
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1057/s41599-023-02149-0
- URN
-
urn:nbn:de:101:1-2024020112214220798443
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:36 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Fu, Yangqi
- Zhu, Yuchun
- SpringerLink (Online service)
Other Objects (12)
Application of a bias-corrected meta-frontier approach and an endogenous switching regression to analyze the technical efficiency of conservation tillage for wheat in South Asia
Bias-corrected estimation in mildly explosive autoregressions
Fast bias-corrected conductivity mapping using stimulated echoes
Microbial Interaction Network Estimation via Bias-Corrected Graphical Lasso
Bias-corrected quantile regression estimation of censored regression models
Cross-validation of bias-corrected climate simulations is misleading
Cross-validation of bias-corrected climate simulations is misleading
Continental-scale bias-corrected climate and hydrological projections for Australia
Continental-scale bias-corrected climate and hydrological projections for Australia
Significant additional Antarctic warming in atmospheric bias-corrected ARPEGE projections
Bias-corrected confidence intervals in a class of linear inverse problems
Finite sample bias corrected IV estimation for weak and many instruments
Application of a bias-corrected meta-frontier approach and an endogenous switching regression to analyze the technical efficiency of conservation tillage for wheat in South Asia
Bias-corrected estimation in mildly explosive autoregressions
Fast bias-corrected conductivity mapping using stimulated echoes
Microbial Interaction Network Estimation via Bias-Corrected Graphical Lasso
Bias-corrected quantile regression estimation of censored regression models
Cross-validation of bias-corrected climate simulations is misleading
Cross-validation of bias-corrected climate simulations is misleading
Continental-scale bias-corrected climate and hydrological projections for Australia
Continental-scale bias-corrected climate and hydrological projections for Australia
Significant additional Antarctic warming in atmospheric bias-corrected ARPEGE projections
Bias-corrected confidence intervals in a class of linear inverse problems
Finite sample bias corrected IV estimation for weak and many instruments
Application of a bias-corrected meta-frontier approach and an endogenous switching regression to analyze the technical efficiency of conservation tillage for wheat in South Asia
Bias-corrected estimation in mildly explosive autoregressions
Fast bias-corrected conductivity mapping using stimulated echoes
Microbial Interaction Network Estimation via Bias-Corrected Graphical Lasso
Bias-corrected quantile regression estimation of censored regression models
Cross-validation of bias-corrected climate simulations is misleading
Cross-validation of bias-corrected climate simulations is misleading
Continental-scale bias-corrected climate and hydrological projections for Australia
Continental-scale bias-corrected climate and hydrological projections for Australia
Significant additional Antarctic warming in atmospheric bias-corrected ARPEGE projections
Bias-corrected confidence intervals in a class of linear inverse problems