A Synthetic Business Cycle Approach to Counterfactual Analysis with Nonstationary Macroeconomic Data

TL;DR

This paper introduces a synthetic business cycle method for causal inference in macroeconomics, separating trend and cycle components to improve counterfactual predictions and avoid spurious correlations.

Contribution

It proposes a novel framework that explicitly decomposes macroeconomic data into trend and cyclical parts, enhancing the robustness of synthetic control methods.

Findings

Improved counterfactual accuracy in macroeconomic case studies

Avoidance of spurious correlations in nonstationary data

Enhanced robustness of causal inference methods

Abstract

This paper investigates the use of synthetic control methods for causal inference in macroeconomic settings when dealing with possibly nonstationary data. While the synthetic control approach has gained popularity for estimating counterfactual outcomes, we caution researchers against assuming a common nonstationary trend factor across units for macroeconomic outcomes, as doing so may result in misleading causal estimation-a pitfall we refer to as the spurious synthetic control problem. To address this issue, we propose a synthetic business cycle framework that explicitly separates trend and cyclical components. By leveraging the treated unit's historical data to forecast its trend and using control units only for cyclical fluctuations, our divide-and-conquer strategy eliminates spurious correlations and improves the robustness of counterfactual prediction in macroeconomic applications. As empirical illustrations, we examine the cases of German reunification and the handover of Hong Kong, demonstrating the advantages of the proposed approach.