Estimation, Inference, and Specification Testing for Possibly Misspecified Quantile Regression

By Tae-Hwan Kim, Halbert White. 2002

163 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ca8d2ee83706629ff593391e7ecd8c22eebbef13

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In Statistics and Probability

Citations in the corpus

VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles (2015, unknown): “…by Assumptions 1 − 6, implies that  12 ||ˆ  −  * || =   (1) This, together with Assumptions 2(iii,iv) and 5(iii.b), enables us to apply the same techniques used in Kim and White (2003) to show  1 =   (1),  2 =   (1) and  3 =   (1), implying   =   (1) It remains to show …”
ROBUST BACKTESTING TESTS FOR VALUE-AT-RISK MODELS (2011, methodology): “Estimators that under correct specification had a martingale difference influence function generally lose this property when model risk is present, see e.g. the quantile regression estimator analyzed in Kim and White (2003).”
Revisiting the role of tourism and globalization in environmental degradation in China: Fresh insights from the quantile ARDL approach (2020, unknown): “( ) ∑ ( ) ∑ ( ) ∑ ( ) ∑ ( ) ∑ ( ) ( ) (2) where, ( ) ( ) (Kim and White, 2003) and 0 > < 1 shows quantile.”
How oil prices, gold prices, uncertainty and risk impact Islamic and conventional stocks? Empirical evidence from QARDL technique (2020, methodology): “The model shown in Equation (1) was extended by Cho et al. (2015) in the form of quantile and suggested the following QARDL(p,q,r,s,u) form: where, ε t ð τ Þ ¼ SP t (cid:0) Q SPt ð τ = δ t (cid:0) 1 Þ (Kim and White, 2003) and 0 > τ < 1 is showing quantile.”
Quantile Regression Under Misspecification, with an Application to the U.S. Wage Structure (2006, unknown): “Finally, it is useful to define a remainder term Rτ (X) = Qτ (Y |X)−X ′ 1β1(τ), (24)”
Specification Tests of Parametric Dynamic Conditional Quantiles (2010, unknown): “E Assumption A5 : Under H A ; (i) there exists a (cid:18) 1 2 B such that k n 1 k B = o P (1); (ii) E [(cid:9) ( e t ( (cid:18) 1 ( ))) exp( i I t 1 )] is di⁄erent from zero in a subset with positive Lebesgue measure on : See Kim and White (2003) for conditions on (cid:18) KB;n to satisfy Assumption”