In this paper, we consider standard as well as instrumental variables regression. Specification problems related to autocorrelation, heteroskedasticity, neglected non-linearity, unsatisfactory out-of-small performance and endogeneity can be addressed in the context of multi-criteria optimization. The new technique performs well, it minimizes all these problems simultaneously, and eliminates them for the most part. Markov Chain Monte Carlo techniques are used to perform the computations. An empirical application to NASDAQ returns is provided.

中文翻译：

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回归中的多准则优化

在本文中，我们考虑标准变量和工具变量的回归。与自相关，异方差，被忽略的非线性，不满意的过小性能和内生性有关的规范问题可以在多标准优化的背景下解决。新技术性能良好，可以同时将所有这些问题最小化，并在很大程度上消除了这些问题。马尔可夫链蒙特卡罗技术用于执行计算。提供了对纳斯达克回报的经验应用。