Multi-criteria optimization in regression
提出一种多准则优化方法，同时处理回归中的自相关、异方差、非线性、样本外表现差和内生性等问题，并用马尔可夫链蒙特卡洛计算，应用于纳斯达克收益率数据。
Abstract 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.