no code implementations • 26 Feb 2023 • Emir Malikov, Shunan Zhao
We develop a novel methodology for the proxy variable identification of firm productivity in the presence of productivity-modifying learning and spillovers which facilitates a unified "internally consistent" analysis of the spillover effects between firms.
no code implementations • 26 Feb 2023 • Emir Malikov, Jingfang Zhang, Shunan Zhao, Subal C. Kumbhakar
Motivated by the long-standing interest in understanding the role of location for firm performance, this paper provides a semiparametric methodology to accommodate locational heterogeneity in production analysis.
no code implementations • 26 Feb 2023 • Emir Malikov, Shunan Zhao, Jingfang Zhang
There is growing empirical evidence that firm heterogeneity is technologically non-neutral.
no code implementations • ICLR 2018 • Shunan Zhao, Chundi Lui, Maksims Volkovs
This paper proposes a new model for document embedding.
no code implementations • 11 Nov 2017 • Chundi Liu, Shunan Zhao, Maksims Volkovs
We propose a new model for unsupervised document embedding.