Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models

By Zongwu Cai, Xiaoping Xu. 2008

230 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4b1fa128c0b2f32a179c6b6b5738adc8e13439ce

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Citations in the corpus

Quantile regression in partially linear varying coefficient models (2009, methodology): “Cai and Xu [2] considered local polynomial estimators for time series data.”
Local quantile regression (2010, methodology): “An important exception is the method for local nonparametric kernel smoothing by Yu and Jones (1998) and Cai and Xu (2008).”
Nonlinear Panel Data Estimation via Quantile Regressions (2015, methodology): “Although we will not analyze this setup in detail, one could adapt our estimation approach by using locally weighted check function (Chaudhuri, 1991, Cai and Xu, 2008).”
Estimating Structural Changes in Regression Quantiles (2011, unknown): “From a methodological perspective, this paper is related to the literature of functional coe¢ cient quantile regression models, see Cai and Xu (2008) and Kim (2007).”
Semiparametric quantile regression estimation in dynamic models with partially varying coefficients (2012, methodology): “For the second result, similar to the method used in the proofs of Lemma 4 and Theorem 1 in Cai and Xu (2008), it follows from Lemma 2 that n Var ( H(1)n ) = τ(1− τ)E [ eT1 (Ω ∗(Ui)) −1Ω(Ui)(Ω ∗(Ui)) −1e1 ] +2 n−1∑ s=1 (1− s n )Cov(h(1)n (ξ1), h (1) n (ξs+1)) = Σβ + o(1).”
Nonparametric estimation of conditional VaR and expected shortfall (2008, background): “But in the bias term of Theorem 3, the quantity S0,2(νp(x) | x)/f (νp(x) | x) replaces ν ′′p (x), which is in the bias term of the ‘‘check’’ function type local linear estimator in Yu and Jones (1998) for iid data and Cai and Xu (2008) for time series.”
Local linear quantile estimation for nonstationary time series (2009, background): “In the context of quantile estimation for stationary processes, Cai and Xu (2005) argued that the local polynomial estimates have a better performance than the local constant estimates.”
Quantile regression with varying coefficients (2007, background): “However, a varying-coefficient model for the conditional quantiles was considered only recently by [18] and [3], where local polynomials were used for independent and time series data, respectively.”