Identification in Linear Quantile Panel Models

2026-09-09

AI Summary

本文研究了在固定且少量时间周期下，线性分位数面板模型中无限制个体异质性的识别问题，通过严格的外生性条件解决。

Abstract

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

Problem

• linear quantile panel models
• unrestricted individual heterogeneity
• fixed and small number of time periods
• strict exogeneity

Innovation

• Linear Quantile Panel Models
• Unrestricted Individual Heterogeneity
• Strict Exogeneity