arXiv 9 Aug 2026 · Econometrics
arXiv:2608.08750 · PDF · Extracted main text
This paper studies a linear panel model with an unrestricted individual effect and a time- stationary idiosyncratic disturbance. We first show that stationarity is a strong restriction in a quantile model. In a linear conditional quantile specification with quantile-dependent slopes, equality of the conditional residual distributions across periods generically forces the slope coefficient to be constant over the quantile index. Thus, a stationary-error model identifies a common location coefficient rather than a collection of quantile-specific slope effects. We then develop a fixed-T estimator of this common coefficient. For each period, we run a cross- sectional quantile regression of the outcome on the full history of regressors. Stationarity makes the quantile projection of the composite individual effect and disturbance common across the period-specific regressions. Differences between diagonal and off-diagonal blocks of the resulting projection coefficients therefore identify the common slope whenever T>=2. We combine all such restrictions by a two-step minimum-distance estimator. The estimator is root-n-consistent and asymptotically normal with fixed T, permits unrestricted dependence across periods within an individual, and does not estimate the individual effects. We provide a consistent analytic covariance estimator, a cluster bootstrap, and an overidentification test of the projection restrictions implied by stationarity. Extensive Monte Carlo experiments show adequate performance under various designs.
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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 | Chamberlain, G (1982) Multivariate Regression Models for Panel Data | 0.644 | 2 | 2 | 100% |
| 2 | Angrist, J. D., V. Chernozhukov, and I. Fernández-Val (2006) Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure | 0.405 | 1 | 1 | 100% |
| 3 | Arellano, M., and S. Bonhomme (2016) Nonlinear Panel Data Estimation via Quantile Regressions | 0.405 | 1 | 1 | 100% |
| 4 | Besstremyannaya, G. and S. Golovan (2019) “Reconsideration of a Simple Approach to Quantile Regression for Panel Data," | 0.405 | 1 | 1 | 100% |
| 5 | Botosaru, I., and C. Muris (2025) Identification of Time-Varying Counterfactual Parameters in Nonlinear Panel Models | 0.405 | 1 | 1 | 100% |
| 6 | Canay, I. A (2011) A Simple Approach to Quantile Regression for Panel Data | 0.405 | 1 | 1 | 100% |
| 7 | Chen, S., and X. Wang (2018) Semiparametric Estimation of Panel Data Models without Monotonicity or Separability | 0.405 | 1 | 1 | 100% |
| 8 | Chernozhukov, V., I. Fernández-Val, J. Hahn, and W. K. Newey (2013) Average and Quantile Effects in Nonseparable Panel Models | 0.405 | 1 | 1 | 100% |
| 9 | Chernozhukov, V., I. Fernández-Val, S. Hoderlein, H. Holzmann, and W… (2015) Nonparametric Identification in Panels Using Quantiles | 0.405 | 1 | 1 | 100% |
| 10 | Firpo, S., A. F. Galvao, C. Pinto, A. Poirier, and G. Sanroman (2022) GMM Quantile Regression | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.