The Co-Integrated Vector Autoregression with Errors–in–Variables
将协整向量自回归模型扩展到变量存在经典测量误差的情形，提出基于状态空间形式的加速EM算法进行估计，并通过模拟和零息收益率曲线应用展示了方法的有效性及忽略测量误差的后果。
The co-integrated vector autoregression is extended to allow variables to be observed with classical measurement errors (ME). For estimation, the model is parametrized as a time invariant state-space form, and an accelerated expectation-maximization algorithm is derived. A simulation study shows that (i) the finite-sample properties of the maximum likelihood (ML) estimates and reduced rank test statistics are excellent (ii) neglected measurement errors will generally distort unit root inference due to a moving average component in the residuals, and (iii) the moving average component may–in principle–be approximated by a long autoregression, but a pure autoregression cannot identify the autoregressive structure of the latent process, and the adjustment coefficients are estimated with a substantial asymptotic bias. An application to the zero-coupon yield-curve is given.