Arbeitspapier
Conditional Quantile Estimators: A Small Sample Theory
We study the small sample properties of conditional quantile estimators such as classical and IV quantile regression. First, we propose a higher-order analytical framework for comparing competing estimators in small samples and assessing the accuracy of common inference procedures. Our framework is based on a novel approximation of the discontinuous sample moments by a Hölder-continuous process with a negligible error. For any consistent estimator, this approximation leads to asymptotic linear expansions with nearly optimal rates. Second, we study the higher-order bias of exact quantile estimators up to O (1/n). Using a novel non-smooth calculus technique, we uncover previously unknown non-negligible bias components that cannot be consistently estimated and depend on the employed estimation algorithm. To circumvent this problem, we propose a “symmetric” bias correction, which admits a feasible implementation. Our simulations confirm the empirical importance of bias correction.
- Sprache
-
Englisch
- Erschienen in
-
Series: CESifo Working Paper ; No. 9046
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation
- Thema
-
non-smooth estimators
KMT coupling
Hungarian construction
higher-order asymptotic distribution
higher-order stochastic expansion
order statistic
bias correction
mixed integer linear programming (MILP)
exact estimators
k-step estimators
quantile
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Franguridi, Grigory
Gafarov, Bulat
Wüthrich, Kaspar
- Ereignis
-
Veröffentlichung
- (wer)
-
Center for Economic Studies and Ifo Institute (CESifo)
- (wo)
-
Munich
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Franguridi, Grigory
- Gafarov, Bulat
- Wüthrich, Kaspar
- Center for Economic Studies and Ifo Institute (CESifo)
Entstanden
- 2021
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