Asymptotics for panel quantile regression models with individual effects

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Quantile regression with nonadditive fixed effects (2022, methodology): “…data estimators with additive fixed effects, including Koenker (2004), Harding and Lamarche (2009), Lamarche (2010), Canay (2011), Besstremyannaya and Golovan (2019), Galvao (2011), Ponomareva (2011), Kato et al. (2012), Rosen (2012), Galvao and Wang (2015), and Machado and Santos Silva (2019).”
Quantiles via moments (2019, methodology): “As noted by Hahn and Newey (2004), the fact that the variance of the estimator depends on n but the bias does not, may lead to confidence intervals with poor coverage in applications where n/T is large (see also Kato, Galvão, and Montes-Rojas, 2012).”
A quantile correlated random coefficients panel data model (2016, background): “As in the regular case our estimate is the solution to (32).”
The benefits are at the tail: Uncovering the impact of macroprudential policy on growth-at-risk (2020, background): “Kato et al. (2012) study how the relationship between the size of N and T is key to guarantee unbiased and asymptotic estimates in panel quantile regressions with individual effects, finding that the main problems arise when T is small.”