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An Empirical Quantile Function for Linear Models with iid Errors
Abstract
The regression quantile statistics of Koenker and Bassett (1978) are employed to construct an estimate of the error quantile function in linear models with iid errors. Some finite sample properties and the asymptotic behavior of the proposed estimator are derived. Comparisons with procedures based on residuals are made. The stackloss data of Brownlee (1965) is reanalyzed to illustrate the technique
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Cited by
Robust Tests for Heteroscedasticity Based on Regression Quantiles
Koenker, R., & Bassett, G. (1982) · Econometrica · doi:10.2307/1912528 · Cited by 1,629
Self-citationUnit Root Quantile Autoregression Inference
Koenker, R., & Xiao, Z. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000001114 · Cited by 512
Self-citationESTIMATING EFFECTS OF LIMITING FACTORS WITH REGRESSION QUANTILES
Cade, B. S., Terrell, J. W., & Schroeder, R. L. (1999) · Ecology · doi:10.1890/0012-9658(1999)080[0311:eeolfw]2.0.co;2 · Cited by 498
An Examination of the Robustness of the Weekend Effect
Connolly, R. A. (1989) · The Journal of Financial and Quantitative Analysis · doi:10.2307/2330769 · Cited by 437
Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study
Buchinsky, M. (1995) · Journal of Econometrics · doi:10.1016/0304-4076(94)01652-g · Cited by 434
Metadata
- Authors
- Bassett, G., & Koenker, R.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1982-06
- Volume
- 77
- Issue
- 378
- Pages
- 407-415
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2083918790
- Cited by
- 212
- References
- 0
- FWCI
- 3.32
- Type
- journal article
- Language
- en
- Open access
- closed
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