Nonlinear Panel Data Estimation via Quantile Regressions

By M. Arellano, S. Bonhomme. 2015

84 citations recorded by the source corpus.

We introduce a class of quantile regression estimators for short panels. Our framework covers static and dynamic autoregressive models, models with general predetermined regressors, and models with multiple individual effects. We use quantile regression as a flexible tool to model the relationships between outcomes, covariates, and heterogeneity. We develop an iterative simulation-based approach for estimation, which exploits the computational simplicity of ordinary quantile regression in each iteration step. Finally, an application to measure the effect of smoking during pregnancy on children’s birthweights completes the paper.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ee0dc83f867759a65da91e918a541e36b6d5be2a

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Citations in the corpus

Quantile regression with nonadditive fixed effects (2022, unknown): “A related literature develops quantile panel data methods with correlated random effects (Geraci andBottai 2007;Abrevaya andDahl 2008;Grahamet al. 2015;Arellano and Bonhomme 2016; Cai et al. 2018; Harding et al. 2020).”
The benefits are at the tail: Uncovering the impact of macroprudential policy on growth-at-risk (2020, methodology): “Cecchetti and Li (2008) use this method to study the impact of asset prices on the distribution of inflation a GDP growth, while De Nicc lo and Lucchetta (2017) identify that t is methodology provides more accurate forecasts of GDP downside risk than traditional VAR and FAVAR models.”
Common correlated effects estimation of heterogeneous dynamic panel quantile regression models (2020, unknown): “With the exception of Galvao (2011) and Arellano and Bonhomme (2016), the literature has focused on estimating static models.”
Panel data quantile regression with grouped fixed effects (2018, methodology): “ They consider a correlated random-eect model in the spirit of Chamberlain (1982) where the individual eects are modeled through a linear regression of some covariates. This is further developed in Arellano and Bonhomme (2016) and Chetverikov, Larsen, and Palmer (2016) where the conditional quantile”
A quantile correlated random coefficients panel data model (2016, methodology): “Equations (5), (6) and (7) show how our semiparametric model arises as a strict general-”