Quantile regression for longitudinal data

By Roger W. Koenker. 2004

2,125 citations recorded by the source corpus.

The penalized least squares interpretation of the classical random effects estimator suggests a possible way forward for quantile regression models with a large number of "fixed effects". The introduction of a large number of individual fixed effects can significantly inflate the variability of estimates of other covariate effects. Regularization, or shrinkage of these individual effects toward a common value can help to modify this inflation effect. A general approach to estimating quantile regression models for longitudinal data is proposed employing l 1 regularization methods. Sparse linear algebra and interior point methods for solving large linear programs are essential computational tools.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/25e2a6d8729580fd0717023b820126a797e900a0

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Citations in the corpus

Talent and/or Popularity: What Does It Take to Be a Superstar? (2012, neutral): “The application of quantile methods to panel data can be problematic (Koenker 2004).”
The benefits are at the tail: Uncovering the impact of macroprudential policy on growth-at-risk (2020, methodology): “growth (see Hamilton, 1989; Pri iceri, 2005; Smith an Vahey, 2016; for two-state Markov chain, Bayesian VAR and C pula estimates, respectively), quantile regressions (Koenker and Basset, 1978) offer an e sy and flexible method in terms of computation effort and requirements of assumptions. These pro”
Quantile regression with nonadditive fixed effects (2022, neutral): “A growing literature has developed quantile panel 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…”
Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India (2013, background): “Following Koenker (2004), when the number of observations on each individual district is not large, it can only be meaningful to take into account the group (here, agro-climatic zone) specific source of unobserved heterogeneity.”
Estimating and Testing a Quantile Regression Model with Interactive Effects (2012, methodology): “The recent literature on panel data quantile regression proposes to estimate a vector of N (nuisance) individual effects (see, e.g., Koenker, 2004; Lamarche, 2010).”