Propensity score with factor loadings: the effect of the Paris Agreement

TL;DR

This paper introduces a new causal inference method using propensity scores based on latent factor loadings for panel data, applied to evaluate the Paris Agreement's impact on European stock returns, offering improved interpretability and efficiency.

Contribution

It proposes a novel inverse propensity score weighting approach that relaxes traditional assumptions and enhances computational efficiency in causal inference from panel data.

Findings

Significant negative short-run effect on green bond firms' stock returns.

Method demonstrates improved causal interpretability and computational efficiency.

Application to Paris Agreement shows policy impact on financial markets.

Abstract

Factor models for longitudinal data, where policy adoption is unconfounded with respect to a low-dimensional set of latent factor loadings, have become increasingly popular for causal inference. Most existing approaches, however, rely on a causal finite-sample approach or computationally intensive methods, limiting their applicability and external validity. In this paper, we propose a novel causal inference method for panel data based on inverse propensity score weighting where the propensity score is a function of latent factor loadings within a framework of causal inference from super-population. The approach relaxes the traditional restrictive assumptions of causal panel methods, while offering advantages in terms of causal interpretability, policy relevance, and computational efficiency. Under standard assumptions, we outline a three-step estimation procedure for the ATT and derive its large-sample properties using Mestimation theory. We apply the method to assess the causal effect of the Paris Agreement, a policy aimed at fostering the transition to a low-carbon economy, on European stock returns. Our empirical results suggest a statistically significant and negative short-run effect on the stock returns of firms that issued green bonds.