Transfer learning for spatial stochastic frontier model

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.