Propensity score with factor loadings: the effect of the Paris Agreement
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| Format: | Preprint |
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2025
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| _version_ | 1866909685326544896 |
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| author | Forino, Angelo Mercatanti, Andrea Morelli, Giacomo |
| author_facet | Forino, Angelo Mercatanti, Andrea Morelli, Giacomo |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_08764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Propensity score with factor loadings: the effect of the Paris Agreement Forino, Angelo Mercatanti, Andrea Morelli, Giacomo Econometrics Applications 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. |
| title | Propensity score with factor loadings: the effect of the Paris Agreement |
| topic | Econometrics Applications |
| url | https://arxiv.org/abs/2507.08764 |