AI 中文总结
研究在数据稀缺环境下局部冲击的全局因素，提出因子增强代理-SVAR方法，通过最小距离法估计政策反应函数恢复政策冲击影响，用意大利NUTS-2地区数据说明该方法可识别并检验，还能获取全局和局部工具。
AI 中文摘要
我们提出了一种新颖的计量经济学方法，用于面板数据中的结构向量自回归（SVAR）并带有外部工具（“代理-SVAR”或“SVAR-IV”），这类面板数据具有强横截面依赖性、动态异质性，且针对感兴趣的冲击的直接外部工具可用性有限。对于每个单位，我们指定一个因子增强代理-SVAR（“代理-FA-SVAR”），它纳入了总结系统中非政策变量横截面信息的因子。然后通过最小距离方法估计特定单位的政策反应函数来间接恢复政策冲击的影响。识别依赖于非政策冲击的全局工具，即面板中所有单位共有的代理，由基于政策和非政策变量因子估计出的单独SVAR内部构建。这些全局工具可辅以从辅助单位级SVAR构建的局部（特异）工具。它们的联合使用使代理-FA-SVAR过度识别且可进行统计检验。我们用年度数据估计意大利NUTS-2地区的政府支出乘数来说明该方法。区域产出冲击的全局和局部工具从布兰查德-佩罗蒂型SVAR获得。
英文摘要
We propose a novel econometric methodology for Structural Vector Autoregressions with external instruments (`proxy-SVARs' or `SVAR-IVs') in panel data characterized by strong cross-sectional dependence, dynamic heterogeneity, and limited availability of direct external instruments for the shocks of interest. For each unit, we specify a Factor-Augmented proxy-SVAR (`proxy-FA-SVAR') that incorporates factors summarizing cross-sectional information from the non-policy variables of the system. The effects of the policy shocks are then recovered indirectly by estimating unit-specific policy reaction functions through a Minimum Distance approach. Identification relies on global instruments for the non-policy shocks; that is, proxies common to all units in the panel, internally constructed from a separate SVAR estimated on factors for the policy and non-policy variables. These global instruments can be complemented with local (idiosyncratic) instruments constructed from auxiliary unit-level SVARs. Their joint use renders the proxy-FA-SVARs overidentified and therefore statistically testable. We illustrate the methodology by estimating government spending multipliers for Italian NUTS-2 regions using annual data. The global and local instruments for the regional output shocks are obtained from Blanchard-Perotti-type SVARs.
