Control VAR: a counterfactual based approach to inference in macroeconomics

TL;DR

This paper introduces control-VAR, a novel causal inference method in macroeconomics that uses control variables to estimate effects without relying on independence assumptions, demonstrated through natural disaster impacts.

Contribution

The paper proposes control-VAR, an innovative approach for causal inference in VAR models that relaxes independence assumptions by incorporating control variables.

Findings

Natural disasters negatively impact the US economy.

Control-VAR provides more credible causal estimates.

Results challenge previous positive impact assumptions.

Abstract

This paper addresses the challenges of giving a causal interpretation to vector autoregressions (VARs). I show that under independence assumptions VARs can identify average treatment effects, average causal responses, or a mix of the two, depending on the distribution of the policy. But what about situations in which the economist cannot rely on independence assumptions? I propose an alternative method, defined as control-VAR, which uses control variables to estimate causal effects. Control-VAR can estimate average treatment effects on the treated for dummy policies or average causal responses over time for continuous policies. The advantages of control-based approaches are demonstrated by examining the impact of natural disasters on the US economy, using Germany as a control. Contrary to previous literature, the results indicate that natural disasters have a negative economic impact without any cyclical positive effect. These findings suggest that control-VARs provide a viable alternative to strict independence assumptions, offering more credible causal estimates and significant implications for policy design in response to natural disasters.