Nonlinear Panel Data Estimation via Quantile Regression

Manuel Arellano ^[2] ; Stéphane Bonhomme ^[1]
1. [1] University of Chicago
  
  University of Chicago
  
  City of Chicago, Estados Unidos
2. [2] CEMFI
Localización: Documentos de Trabajo ( CEMFI ), Nº. 5 (CEMFI Working Paper 1505, July 2015), 2015
Idioma: inglés
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Resumen
- 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.