Tobit models in strategy research: Critical issues and applications
回顾了Tobit模型在管理研究中的应用，讨论了数据假设、删失与选择偏差的混淆以及残差分布假设违反等挑战，并通过进口竞争与产业多元化的实证分析比较了不同估计方法，为处理删失数据提供了指南。
Abstract Research Summary Tobit models have been used to address several questions in management research. Reviewing existing practices and applications, we discuss three challenges: (a) assumptions about the nature of data, (b) apparent interchangeability between censoring and selection bias, and (c) potential violations of key assumptions in the distribution of residuals. Empirically analyzing the relationship between import competition and industry diversification, we contrast Tobit models with results from other estimators and show the conditions that make Tobit a suitable empirical approach. Finally, we offer suggestions and guidelines on how to use Tobit models to deal with censored data in strategy research. Managerial Summary Data on strategic decisions often exhibit certain features, such as excess zeros and values bounded within a given range, which complicate the use of linear econometric techniques. Deriving statistical evidence in such instances may suffer from biases that undermine managerial applications. Our study presents an extensive comparison of different econometric models to deal with censored data in strategic management showing the strengths and weaknesses of each model. We also conduct an application to the context of import penetration and industry diversification to highlight how the relationship between these two variables changes depending on the econometric model used for the analysis. In conclusion, we provide a set of recommendations for scholars interested in censored data.