🤖 AI Summary
This study addresses the challenge of identifying local fiscal policy shocks in panel settings characterized by scarce data, strong cross-sectional dependence, and dynamic heterogeneity, where direct external instrumental variables are typically unavailable. The authors propose a factor-augmented proxy structural vector autoregression (proxy-FA-SVAR) that combines global common factors with region-specific local instruments to construct an over-identifying strategy. Within a factor-augmented SVAR-IV framework, they employ minimum distance estimation and Blanchard-Perotti–type restrictions to effectively recover regional fiscal response functions. The approach demonstrates testability and robustness in finite samples and is applied to estimate annual government spending multipliers across Italian NUTS-2 regions, offering a novel paradigm for analyzing heterogeneous fiscal effects.
📝 Abstract
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.