研究團隊於學術期刊「國際電子與計算機科學進展雜誌」（International Journal of Advances in Electronics and Computer Science）發表題為「在數據包絡分析中為有限數據的輸入選擇代理」（Selecting Proxies for Inputs with Limited Data in Data Envelopment Analysis）的學術文章，建立於數據包絡分析中為有限數據選擇輸入代理的方法。有關研究為研究資助局2021/2022年度本地自資學位界別競逐研究資助計劃下的「教員發展計劃」資助項目「結構效率測量的統一框架：理論與應用」（UGC/FDS15/E02/21）成果的一部分。
論文摘要（英文）：
Model selection is an important issue in Data Envelopment Analysis. A specific case is choosing proxies for inputs/ outputs when the required data are not available. When there are several potential candidates in the data that can capture the characteristics of a theoretical variable, the researcher usually decides a proxy by experience. However, choosing by experience is usually seen as subjective decisions and lack of theoretical grounds. This paper adopts the principle of the benefit of doubt to explore systematic ways of selecting a proper proxy for an input/ output.
We observe that this line of literature selects a proxy by choosing the candidate that causes the data closer to the empirical production frontier. Following this line of research, this paper suggests three approaches to find a proxy from several candidates. When a candidate dominates other candidates as a proxy for a variable, our method will select this candidate objectively.
All approaches discussed in this paper are applied to 3 industries in China from 2017 to 2019. To select an input proxy for capital, there are three alternatives: total assets, non-current assets and current assets. Although non-current assets may be expected to be an appropriate proxy for capital, it is overwhelmingly outperformed by total assets and current assets. Since these three data variables are the most common data available in published data as proxies for capital, our empirical results are valuable to applied researchers of the Chinese economy.