Downloads
CWP191717.pdf
PDF | 491.72 KB
The impact of measurement error in explanatory variables on quantile regression functions is investigated using a small variance approximation. The approximation shows how the error contaminated and error free quantile regression functions are related. A key factor is the distribution of the error free explanatory variable. Exact calculations probe the accuracy of the approximation. The order of the approximation error is unchanged if the density of the error free explanatory variable is replaced by the density of the error contaminated explanatory variable which is easily estimated. It is then possible to use the approximation to investigate the sensitivity of estimates to varying amounts of measurement error.
Authors
Research Fellow University College London
Andrew is the Director of the ESRC Centre for Microdata Methods and Practice (cemmap) and Professor of Economics and Economic Measurement at UCL.
Working Paper details
- DOI
- 10.1920/wp.cem.2017.1917
- Publisher
- The IFS
Suggested citation
Chesher, A. (2017). Understanding the effect of measurement error on quantile regressions. London: The IFS. Available at: https://ifs.org.uk/publications/understanding-effect-measurement-error-quantile-regressions (accessed: 1 July 2024).
More from IFS
Understand this issue
Gender norms, violence and adolescent girls’ trajectories: Evidence from India
24 October 2022
What are the challenges in getting debt on a falling path?
28 June 2024
Election Special: Your questions answered
27 June 2024
Policy analysis
IFS Deputy Director Carl Emmerson appointed to the UK Statistics Authority Methodological Assurance Review Panel
14 April 2023
ABC of SV: Limited Information Likelihood Inference in Stochastic Volatility Jump-Diffusion Models
We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Compu- tation which build likelihoods based on limited information.
12 August 2014
Is there really an NHS productivity crisis?
17 November 2023
Academic research
Inference for rank-rank regressions
28 May 2024
Sample composition and representativeness on Understanding Society
2 February 2024
The impact of labour demand shocks when occupational labour supplies are heterogeneous
28 June 2024