NONPARAMETRIC COINTEGRATING REGRESSION WITH ENDOGENEITY AND LONG MEMORY
存在内生性与长记忆的非参数协整回归AI 解读 ↓
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存在内生性与长记忆的非参数协整回归
- 研究问题
- 当协整回归具有非线性关系、序列相关误差及内生解释变量，并允许长记忆创新时，非参数估计和设定检验是否有效？
- 方法与思路
- 作者分析局部常数核估计、参数估计及模型设定检验的渐近性质，并以模拟考察强内生性和长记忆下的表现。
- 材料陈述的贡献
- 摘要称核估计仍一致且渐近混合正态；设定检验可对内生性稳健，但长记忆下非枢轴，收敛率依赖参数。
- 阅读关注点
- 应核对内生性与记忆程度的允许范围、核估计的归一化方式，以及检验涉及分数布朗运动局部时的临界值处理。
- 阅读边界
- 仅据所给 Crossref 摘要；具体正则条件、参数依赖的收敛率和检验实施办法未说明，不能推断其他版本变化。
原文摘要
This paper explores nonparametric estimation, inference, and specification testing in a nonlinear cointegrating regression model where the structural equation errors are serially dependent and where the regressor is endogenous and may be driven by long memory innovations. Generalizing earlier results of Wang and Phillips (2009a,b, Econometric Theory 25, 710–738, Econometrica 77, 1901–1948), the conventional nonparametric local level kernel estimator is shown to be consistent and asymptotically (mixed) normal in these cases, thereby opening up inference by conventional nonparametric methods to a wide class of potentially nonlinear cointegrated relations. New results on the consistency of parametric estimates in nonlinear cointegrating regressions are provided, extending earlier research on parametric nonlinear regression and providing primitive conditions for parametric model testing. A model specification test is studied and confirmed to provide a valid mechanism for testing parametric specifications that is robust to endogeneity. But under long memory innovations the test is not pivotal, its convergence rate is parameter dependent, and its limit theory involves the local time of fractional Brownian motion. Simulation results show good performance for the nonparametric kernel estimates in cases of strong endogeneity and long memory, whereas the specification test is shown to be sensitive to the presence of long memory innovations, as predicted by asymptotic theory.