Testing for Cointegration in Linear Quadratic Models
用蒙特卡洛方法评估线性二次模型中多种协整检验的小样本表现，发现调整成本项和回归元数量较大时检验能力差异显著，总体而言增广迪基-富勒检验和菲利普斯的Zα、Zt检验在检验水平和功效上最可靠。
This article evaluates the finite-sample performance of various tests for cointegration by Monte Carlo methods. The evaluation takes place within the linear quadratic model. The results indicate sharp differences in the ability of the tests to detect cointegrating relations, especially when the cost-of-adjustment term and the number of regressors are large. Although no single test dominates for all the parameter settings considered, overall the augmented Dickey-Fuller, Z α and Zt tests of Phillips seem the most reliable in terms of test size and power.