.png){kind=icon}
.png){kind=icon}
Return
Transfer learning for spatial stochastic frontier model
.png){kind=icon}
PRE
AI
DOI：10.1080/17421772.2025.2597818.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The spatial stochastic frontier model approaches to evaluate firm-level productivity and maximum potential output, while accounting for the influence of neighbouring geographical areas or economies. Existing studies, however, overlook small-sample scenarios. We propose integrating machine learning with traditional statistical methods to address this gap. Specifically, transfer learning which leverages knowledge from related tasks is introduced to enhance estimation under limited data. Novel algorithms are developed for cases with known or unknown source datasets, aiming to improve parameter estimation efficiency by addressing spatial correlation and endogeneity in small samples.
Keywords:
Spatial lagged stochastic frontier model
transfer learning
2SLS estimation
regularisation
C21
C13
C24
L25
Journal
S
IF:
2.2
Papers:
55
Citations:
953