arXiv 6 May 2024 · Econometrics · publishedEconometric Reviews (2025)
arXiv:2405.03826 · PDF · DOI · OpenAlex · Extracted main text
I propose a quantile-based nonadditive fixed effects panel model to study heterogeneous causal effects. Similar to standard fixed effects (FE) model, my model allows arbitrary dependence between regressors and unobserved heterogeneity, but it generalizes the additive separability of standard FE to allow the unobserved heterogeneity to enter nonseparably. Similar to structural quantile models, my model's random coefficient vector depends on an unobserved, scalar ”rank” variable, in which outcomes (excluding an additive noise term) are monotonic at a particular value of the regressor vector, which is much weaker than the conventional monotonicity assumption that must hold at all possible values. This rank is assumed to be stable over time, which is often more economically plausible than the panel quantile studies that assume individual rank is iid over time. It uncovers the heterogeneous causal effects as functions of the rank variable. I provide identification and estimation results, establishing uniform consistency and uniform asymptotic normality of the heterogeneous causal effect function estimator. Simulations show reasonable finite-sample performance and show my model complements fixed effects quantile regression. Finally, I illustrate the proposed methods by examining the causal effect of a country's oil wealth on its military defense spending.
appendix boundary found by appendix_command · 53% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Canay, Ivan A (2011) A Simple Approach to Quantile Regression for Panel Data | 0.979 | 16 | 5 | 94% |
| 2 | Chernozhukov, Victor and Hansen, Christian (2005) An IV Model of Quantile Treatment Effects | 0.874 | 12 | 2 | 100% |
| 3 | Powell, David (2022) Quantile Regression with Nonadditive Fixed Effects | 0.874 | 6 | 2 | 100% |
| 4 | Athey, Susan and Imbens, Guido W (2006) Identification and Inference in Nonlinear Difference-in-Differences Models | 0.737 | 3 | 2 | 100% |
| 5 | Hausman, Jerry and Liu, Haoyang and Luo, Ye and Palmer, Christopher (2021) Errors in the Dependent Variable of Quantile Regression Models | 0.737 | 3 | 2 | 100% |
| 6 | Cotet, Anca M. and Tsui, Kevin K (2013) Oil and Conflict: What Does the Cross Country Evidence Really Show? | 0.693 | 8 | 1 | 100% |
| 7 | Arellano, Manuel and Bonhomme, Stéphane (2016) Nonlinear Panel Data Estimation via Quantile Regressions | 0.644 | 2 | 2 | 100% |
| 8 | Chernozhukov, Victor and Hansen, Christian (2008) Instrumental variable quantile regression: A robust inference approach | 0.644 | 2 | 2 | 100% |
| 9 | Arellano, Manuel and Bonhomme, Stéphane (2012) Identifying Distributional Characteristics in Random Coefficients Panel Data Models | 0.585 | 3 | 1 | 100% |
| 10 | Chernozhukov, Victor and Fernández-Val, Iván and Hahn, Jinyong and N… (2013) Average and Quantile Effects in Nonseparable Panel Models | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 36 scored citations.