REGRESSION QUANTILES FOR TIME SERIES

By Zongwu Cai. Econometric Theory, 2002

200 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1199c76dce2d8b70914d94dc2b613810b107bac5

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Citations in the corpus

Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models (2008, methodology): “ Cai (2002a) , and then used a projection method to estimate each component, as discussed”
CONFIDENCE BANDS IN QUANTILE REGRESSION (2009, background): “In the literature, according to Fan et al. (1994, 1996), Yu and Jones (1997, 1998), Hall et al. (1999), Cai (2002), and others, asymptotic normality at interior points for various nonparametric smoothers, e.g., local constant, local linear, reweighted Nadaraya–Watson methods, etc., has been shown:…”
Tilted Nonparametric Estimation of Volatility Functions With Empirical Applications (2010, methodology): “Consequently, it is commonly recommended in applications to use the theoretically less satisfactory local constant estimator (also known as the Nadaraya–Watson estimator) when fitting the variance function (Chen and Qin 2002; Porter 2003).”
On Conditional Density Estimation (2003, background): “CAI (2001, 2002) also extended the smoother of Hall et al. (1999) to contexts other than the distribution function mainly to conditional quantiles.”
NONPARAMETRIC ESTIMATION OF CONDITIONAL VALUE-AT-RISK AND EXPECTED SHORTFALL BASED ON EXTREME VALUE THEORY (2016, methodology): “(2007) or Cai and Wang (2008). These estimators for a-CVaR(x) can then be used to produce nonparamet-”
Local linear spatial quantile regression (2009, background): “For N = 1, our problem reduces to the classical one of quantile (auto)regression for independent or serially dependent observations, and has received extensive attention in the literature: see, for instance, [29], [31], [14], [41], [17], [32], [33], [50], [53], [54], [27], [7], as well as [55].”
Nonparametric estimation of conditional VaR and expected shortfall (2008, methodology): “The proof of Lemma 1 can be found in Cai (2002) and the proof of Lemma 2 is relegated to the Appendix.”
Local linear quantile estimation for nonstationary time series (2009, background): “…of Yi given Xi ; see, for instance, Bhattacharya and Gangopadhyay (1990), Chaudhuri (1991), Chapter 6 of Fan and Gijbels (1996) and Yu and Jones (1998) for i.i.d. data and Abberger (1997), Abberger and Heiler (2002), Cai (2002) and Cai and Xu (2005) for stationary strong mixing time series.”
On Additive Conditional Quantiles With High-Dimensional Covariates (2003, methodology): “Our choice of this approach is motivated by our own earlier work on conditional quantiles (see, e.g., De Gooijer et al. 2001, 2002), as well as work of Hall, Wolff, and Yao (1999) and Cai (2002).”
Functional-coefficient models for nonstationary time series data (2009, methodology): “Stationary linear and nonlinear time series or Markov chains fulfilling certain (mild) conditions are α-mixing with exponentially decaying coefficients; see discussions and examples in Cai (2002a), Carrasco and Chen (2002) and Chen and Tang (2005).”
Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions (2013, methodology): “The proof of Theorem 4.1 follows similar arguments as the proof of Theorem 3 of Cai (2002) given the results of Theorem 3.2.”
Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall (2008, methodology): “This is the standard kernel estimator for the cdf, which has been the focus of many studies (e.g. Abberger 1997; Hall, Wolff and Yao 1999; Cai 2002).”
Quantile regression: applications and current research areas (2003, methodology): “However, using recent advances in interior point methods for solving linear programming problems discussed by Portnoy and Koenker (1997), this minimization can be performed by using the algorithm that was provided by Koenker and D’Orey (1987).”
Semiparametric quantile regression estimation in dynamic models with partially varying coefficients (2012, methodology): “Similar to the proof of Theorem 2 in Cai, Fan and Yao (2000) or Theorem 1 in Cai (2002a), by using the small-block and large-block technique and the Cramér-Wold device, one can show (although lengthy and tediously) that A2n d−→ N (0, Σα).”
Conditional quantile analysis when covariates are functions, with application to growth data (2012, methodology): “For the functional quantile linear regression model that was proposed by Cardot et al. (2005), the estimator depends on the number of knots k, the degree q of the spline, the order m of the derivative that is chosen for the penalization term and the smoothing parameter ρ.”