Efficient IV Estimation in Nonstationary Regression
非平稳回归中的有效工具变量估计AI 解读 ↓
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非平稳回归中的有效工具变量估计
- 研究问题
- 当回归变量含平稳与非平稳成分、非平稳性并非满秩时，全修正 IV、GMM 与广义工具变量估计的小样本表现如何？
- 方法与思路
- 作者概述 FM-IV、FM-GMM 与 FM-GIVE，并通过模拟将其与 OLS 和常规 IV 比较，分别考察平稳及非平稳系数。
- 材料陈述的贡献
- 摘要称，FM-GIVE 对平稳系数通常表现较好，三种全修正法对非平稳系数接近；FM-IV 的离群值可能推高均方根误差。
- 阅读关注点
- 应核对平稳成分如何被利用、模拟中的自回归根和工具变量设定，以及集中程度、中心偏差与均方根误差的权衡。
- 阅读边界
- 仅据所给 Crossref 摘要，无法量化效率增益或推广模拟排序；较集中、中心较准确也不保证均方根误差更低。
原文摘要
A limit theory for instrumental variables (IV) estimation that allows for possibly nonstationary processes was developed in Kitamura and Phillips (1992, Fully Modified IV, GIVE, and GMM Estimation with Possibly Non-stationary Regressors and Instruments, mimeo, Yale University). This theory covers a case that is important for practitioners, where the nonstationarity of the regressors may not be of full rank, and shows that the fully modified (FM) regression procedure of Phillips and Hansen (1990) is still applicable. FM. versions of the generalized method of moments (GMM) estimator and the generalized instrumental variables estimator (GIVE) were also developed, and these estimators (FM-GMM and FM-GIVE) were designed specifically to take advantage of potential stationarity in the regressors (or unknown linear combinations of them). These estimators were shown to deliver efficiency gains over FM-IV in the estimation of the stationary components of a model. This paper provides an overview of the FM-IV, FM-GMM, and FM-GIVE procedures and investigates the small sample properties of these estimation procedures by simulations. We compare the following five estimation methods: ordinary least squares, crude (conventional) IV, FM-IV, FM-GMM, and FM-GIVE. Our findings are as follows, (i) In terms of overall performance in both stationary and nonstationary cases, FM-IV is more concentrated and better centered than OLS and crude IV, though it has a higher root mean square error than crude IV due to occasional outliers, (ii) Among FM-IV, FM-GMM, and FM-GIVE, (a) when applied to the stationary coefficients, FM-GIVE generally outperforms FM-IV and FM-GMM by a wide margin, whereas the difference between the latter two is quite small when the AR roots of the stationary processes are rather large; and (b) when applied to the nonstationary coefficients, the three estimators are numerically very close. The performance of the FM-GIVE estimator is generally very encouraging.