具有双向依赖的面板SVAR中的推断
Inference in Panel SVARs with Two-Way Dependence
- University of Duisburg-Essen（杜伊斯堡-埃森大学）
- Georg August University of Göttingen（哥廷根大学）
- Leipzig University（莱比锡大学）
- Humboldt University of Berlin（柏林洪堡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对存在双向依赖的异质性面板SVAR，提出适配均值组估计的闭式合并识别方法与递归设计的面板移动块自助法，模拟验证了联合重采样方案的良好覆盖率表现。
AI中文摘要：
我们针对异质性面板向量自回归（VAR）模型及其结构脉冲响应函数开展推断研究，其中误差项在截面维度和时间维度存在依赖关系（即双向依赖）。对于代理识别的结构VAR，我们首先适配均值组估计方法并构建闭式合并识别。考虑简化形式的VAR动态、残差协方差及结构参数，我们在截面维度和时间维度的联合极限下推导了联合中心极限定理。随后，我们提出一种递归设计的面板移动块自助法，该方法在（i）时间维度、（ii）截面维度或（iii）同时在两个维度联合重采样估计的误差项，并证明了双向依赖下联合面板块方案的一致性。模拟结果显示，联合方案的覆盖率接近名义水平，而截面重采样则存在严重的覆盖率不足问题。
英文摘要：
We develop inference for heterogeneous panel vector autoregressive (VAR) models and their structural impulse response functions, where the error terms are dependent in the cross-sectional and time dimensions (two-way dependence). For proxy-identified structural VARs, we first adapt mean-group estimation and construct a closed-form pooled identification. Considering the reduced-form VAR dynamics, residual covariances, and structural parameters, we derive a joint central limit theorem under joint limits in the cross-sectional and time dimensions. We then propose a recursive-design panel moving-block bootstrap that resamples the estimated error terms in (i) the temporal, (ii) the cross-sectional, or (iii) both dimensions jointly, and prove consistency of the joint panel-block scheme under two-way dependence. Simulations show coverage close to the nominal level for the joint scheme but severe undercoverage for cross-sectional resampling.
