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Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research
Abstract
This paper provides a guideline for the practical use of the semi-parametric technique of quantile regression, concentrating on cross-section applications. It summarizes the most important issues in quantile regression applications and fills some gaps in the literature. The paper (a) presents several alternative estimators for the covariance matrix of the quantile regression estimates; (b) reviews the results for a sequence of quantile regression estimates; and (c) discusses testing procedures for homoskedasticity and symmetry of the error distribution. The various results in the literature are incorporated into the generalized method of moments frame-work. The paper also provides an empirical example using data from the Current Population Survey, raising several important issues relevant to empirical applications of quantile regression. The paper concludes with an extension to the censored quantile regression model.
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Cited by
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Wage Dispersion and Decentralization of Wage Bargaining
Dahl, C. M., le Maire, D., & Munch, J. R. (2013) · Journal of Labor Economics · doi:10.1086/669339 · Cited by 126
Fenwick, E. K., Xie, J., Ratcliffe, J., Pesudovs, K., Finger, R. P., Wong, T. Y., & Lamoureux, E. L. (2012) · Investigative Opthalmology & Visual Science · doi:10.1167/iovs.11-8992 · Cited by 101
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Metadata
- Authors
- Buchinsky, M.
- Venue
- The Journal of Human Resources
- Publisher
- JSTOR
- Published
- 1998
- Volume
- 33
- Issue
- 1
- Pages
- 88
- ISSN
- 0022-166X (ISSN-L)
- OpenAlex
- W1501835916
- Cited by
- 1,449
- References
- 0
- FWCI
- 17.08
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.