Cointegration Testing Using Pseudolikelihood Ratio Tests
提出用伪最大似然估计确定向量自回归模型的协整秩，并通过伪似然比检验进行推断。非高斯伪似然比检验在创新项呈尖峰分布时比Johansen的高斯检验功效更高。
This paper considers pseudomaximum likelihood estimators for vector autoregressive models. These estimators are used to determine the cointegration rank of a multivariate time series process using pseudolikelihood ratio tests. The asymptotic distributions of these tests depend on nuisance parameters if the pseudolikelihood is non-Gaussian. This even holds if the likelihood is correctly specified. The nuisance parameters have a natural interpretation and can be consistently estimated. Some simulation results illustrate the usefulness of the tests: non-Gaussian pseudolikelihood ratio tests generally have a higher power than the Gaussian test of Johansen if the innovations demonstrate leptokurtic behavior.