ESTIMATION AND INFERENCE IN SHORT PANEL VECTOR AUTOREGRESSIONS WITH UNIT ROOTS AND COINTEGRATION
含单位根与协整的短面板向量自回归：估计与推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究短面板 VAR 的估计以及单位根、协整检验，直接对应宏观面板与 VAR 方法。
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含单位根与协整的短面板向量自回归：估计与推断
- 研究问题
- 当面板时间维度短、截面维度大，且变量可能含单位根与协整时，如何估计面板 VAR 并开展可靠的检验？
- 方法与思路
- 作者在随机或固定个体效应设定下构造 GMM 与 QML 估计量，比较其渐近与有限样本性质，并讨论单位根和协整检验。
- 材料陈述的贡献
- 摘要称，结合水平与差分的 GMM 渐近方差依赖个体效应方差，固定效应 QML 避免这一问题，且在所报模拟中往往表现更好。
- 阅读关注点
- 应核对短面板渐近框架、各估计量使用的矩条件与初始值处理，以及 QML 单位根和协整检验的统计量与适用条件。
- 阅读边界
- 仅依据所给 RePEc 出版社摘要；模拟优势限于作者考察的设计，误差与截面依赖等具体条件未说明，也不能推断其他版本变化。
原文摘要
This paper considers estimation and inference in panel vector autoregressions where (i) the individual effects are either random or fixed, (ii) the time-series properties of the model variables are unknown a priori and may feature unit roots and cointegrating relations, and (iii) the time dimension of the panel is short and its cross-sectional dimension is large. Generalized method of moments (GMM) and quasi maximum likelihood (QML) estimators are obtained and compared in terms of their asymptotic and finite-sample properties. It is shown that the asymptotic variances of the GMM estimators that are based on levels in addition to first differences of the model variables depend on the variance of the individual effects, whereas by construction the fixed effects QML estimator is not subject to this problem. Monte Carlo evidence is provided showing that the fixed effects QML estimator tends to outperform the various GMM estimators in finite sample under both normal and nonnormal errors. The paper also shows how the fixed effects QML estimator can be successfully used for unit root and cointegration tests in short panels.We are grateful to Karim Abadir, Stephen Bond, Jinyong Hahn, Marc Nerlove, Ingmar Prucha, and, especially, Manuel Arellano, Peter Schmidt, Peter Phillips (the editor), and four anonymous referees for helpful and constructive comments. We have also benefited from useful suggestions by participants at various seminars and conferences.