# On Policy Evaluation with Aggregate Time-Series Shocks

## TL;DR

This paper introduces a new estimator for policy evaluation using aggregate time-series shocks that effectively addresses unobserved confounding, enabling valid inference in endogenous variable settings.

## Contribution

It proposes a novel data-driven aggregation scheme that eliminates unobserved confounders, improving causal inference with aggregate instruments.

## Key findings

- The estimator is consistent and asymptotically normal under certain conditions.
- Application to Nakamura and Steinsson's data demonstrates its practical effectiveness.
- Provides a framework for valid inference in models with aggregate uncertainty.

## Abstract

We develop an estimator for applications where the variable of interest is endogenous and researchers have access to aggregate instruments. Our method addresses the critical identification challenge -- unobserved confounding, which renders conventional estimators invalid. Our proposal relies on a new data-driven aggregation scheme that eliminates the unobserved confounders. We illustrate the advantages of our algorithm using data from Nakamura and Steinsson (2014) study of local fiscal multipliers. We introduce a finite population model with aggregate uncertainty to analyze our estimator. We establish conditions for consistency and asymptotic normality and show how to use our estimator to conduct valid inference.