Opening the Black Box of Local Projections
Local Projections (LP), widely used for estimating macroeconomic impulse responses to policy shocks, suffer from a “black-box” nature that impedes attribution of responses to specific historical drivers. This paper introduces the first interpretable decomposition framework for LP estimation: it represents impulse responses as weighted sums of historical events, where weights jointly encode standardized shock intensity and temporal proximity. Going beyond linearity, the framework generalizes to nonlinear machine learning models and establishes a cross-model transferable interpretability paradigm. Empirically, applied to monetary policy (Nixon shock), fiscal shocks (WWII), and climate/financial shocks (Mount Agung eruption, bond risk premium fluctuations), the method precisely identifies a small set of pivotal historical events. Results reveal that LP estimates are highly contingent on salient historical nodes, substantially enhancing transparency and verifiability in causal inference.