Bayesian Semi-Nonparametric Arch Models
提出一种贝叶斯半非参数方法处理ARCH模型，能在似然函数受非线性不等式约束时获得小样本结果，并放松了正态误差假设。通过应用和蒙特卡洛研究证明该方法可行且必要。
A Bayesian seminonparametric approach to ARCH models is developed with the advantage that small sample results are obtained even when the likelihood function is subject to nonlinear inequality constraints (as in the ARCH models used in this paper). The seminonparametric nature of the approach allows for the relaxation of the assumption of normal errors. An application and a small Monte Carlo study indicate that the methods the author advocates are both feasible and necessary. Copyright 1994 by MIT Press.