The second-order bias and mean squared error of quantile regression estimators

Author:

Lee Tae-HwyORCID,Ullah Aman,Wang He

Publisher

Springer Science and Business Media LLC

Subject

Economics, Econometrics and Finance (miscellaneous),Economics and Econometrics

Reference51 articles.

1. Amemiya, T. (1980). The $$n^{2}$$-order mean squared errors of the maximum likelihood and the minimum logit chi-square estimators. Annals of Mathematical Statistics, 8, 488–505.

2. Andrews, D. W. K. (1994). Empirical process methods in econometrics. In R. F. Engle & D. L. McFadden (Eds.), Handbook of Econometrics (Vol. 4, pp. 2247–2294). Amsterdam: Elsevier Science.

3. Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20, 651–707.

4. Bahadur, R. R. (1966). A note on quantiles in large samples. The Annals of Mathematical Statistics, 37, 577–581.

5. Bao, Y., & Ullah, A. (2007). The second-order bias and mean squared error of estimators in time-series models. Journal of Econometrics, 140, 650–669.

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