This paper deals with econometric models in which the dependent variable, some explanatory variables, or both are observed as censored interval data. This discretization often happens due to confidentiality of sensitive variables like income. Models using these variables cannot point identify regression parameters as the conditional moments are unknown, which led the literature to use interval estimates. Here, we propose a discretization method through which the regression parameters can be point identified while preserving data confidentiality. We demonstrate the asymptotic properties of the OLS estimator for the parameters in multivariate linear regressions for cross-sectional data. The theoretical findings are supported by Monte Carlo experiments and illustrated with an application to the Australian gender wage gap.

翻译：本文研究因变量、部分解释变量或两者均以截断区间数据形式观测的经济计量模型。这种离散化常因收入等敏感变量的保密性而产生。由于条件矩未知，使用此类变量的模型无法点识别回归参数，因此文献中采用区间估计方法。本文提出一种既能保护数据机密性，又能实现回归参数点识别的离散化方法。我们证明了横截面数据多元线性回归中参数OLS估计量的渐近性质。理论结果通过蒙特卡洛实验得到验证，并应用于澳大利亚性别工资差距分析。