Heterogeneity-robust granular instruments
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Granular instrumental variables (GIV) has experienced sharp growth in empirical macro-finance. The methodology's rise showcases granularity's potential for identification across many economic environments, like the estimation of spillovers and demand systems. I propose a new estimator--called robust granular instrumental variables (RGIV)--that enables studying unit-level heterogeneity in spillovers. Unlike existing methods that assume heterogeneity is a function of observables, RGIV leaves heterogeneity unrestricted. In contrast to the baseline GIV estimator, RGIV allows for unknown shock variances and does not require skewness in the size distribution. I find evidence of unit-level heterogeneity in applications to sovereign yield spillovers and the inelastic markets hypothesis.
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Granular Instrumental Variables in Large Panels: Identification and Inference Across Strong, Nearly Weak, and Weak GIV
Formalizes three regimes of GIV instrument strength in large panels and derives consistency, rates, and inference rules for strong, nearly weak, and weak cases.
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