Estimation of Cointegration Vectors with Linear Restrictions
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带线性约束的协整向量估计
- 研究问题
- 如何估计满足线性约束的协整向量，在不要求外生性的情况下实现识别，并检验参数及过度识别约束？
- 方法与思路
- 作者以秩条件识别受约束协整关系，先估计协整回归系数矩阵，再用广义最小二乘构造单方程与系统估计量。
- 材料陈述的贡献
- 摘要称，估计程序无需外生性假设，并得到混合正态极限分布，可开展渐近卡方 Wald 检验及过度识别约束检验。
- 阅读关注点
- 应核对识别秩条件、非协整序列刻画共同趋势的方式，以及两步估计、系统效率比较和约束检验的适用条件。
- 阅读边界
- 仅据所给 Crossref 摘要，无法完整判断识别及分布结论的条件；无需外生性不意味着无需其他模型假设。
原文摘要
A general approach for the estimation of cointegration vectors with linear restrictions is described. In the special case of zero restrictions, the cointegration relations of the paper are formally similar to the structural form of a traditional simultaneous equation model. The proposed estimation procedures require a conventional rank condition of identification but no exogeneity assumption. In place of exogenous variables there are series that are not cointegrated and can therefore describe the common trends in the system. The estimators of the paper are flexible and simple to use. They can be combined with several recent estimators developed for cointegration regressions which in the present context are formally similar to the reduced form of a simultaneous equation model. After the coefficient matrix of a cointegration regression has been estimated, the estimators of the paper can be obtained by simple generalized least squares. Both single equation estimators and more efficient system estimators are developed. The asymptotic distributions of the estimators are shown to be mixed normal so that Wald tests with asymptotic chi-square distributions under the null hypothesis can be obtained in the usual way. Convenient test procedures for checking the validity of overidentification restrictions are also provided.