Downloads
cwp401515.pdf
PDF | 522.73 KB
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.
Authors
Research Fellow Centre for Monetary and Financial Studies (CEMFI)
Manuel is a Research Fellow of the IFS and a Professor of Econometrics at CEMFI, Madrid.
Professor of Economics University of Chicago
Working Paper details
- DOI
- 10.1920/wp.cem.2015.4015
- Publisher
- Institute for Fiscal Studies
Suggested citation
Arellano, M and Bonhomme, S. (2015). Nonlinear panel data estimation via quantile regressions. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/nonlinear-panel-data-estimation-quantile-regressions (accessed: 26 April 2024).
More from IFS
Understand this issue
Where next for the state pension?
13 December 2023
Social mobility and wealth
12 December 2023
Autumn Statement 2023: IFS analysis
23 November 2023
Policy analysis
Recent trends in and the outlook for health-related benefits
19 April 2024
Progression of nurses within the NHS
12 April 2024
Regional variation in earnings and the retention of NHS staff in Agenda for Change bands 1 to 4
10 April 2024
Academic research
A senior doctor like me: Gender match and occupational choice
24 April 2024
Police infrastructure, police performance, and crime: Evidence from austerity cuts
24 April 2024
Imagine your life at 25: Gender conformity and later-life outcomes
24 April 2024