Self‐weighted least absolute deviation estimation for infinite variance autoregressive models

By S. Ling. 2005

122 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1b74e9d34d7435aafe022d43ceffd3664efe5505

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Citations in the corpus

Global self-weighted and local quasi-maximum exponential likelihood estimators for ARMA–GARCH/IGARCH models (2011, methodology): “1 in Ling and McAleer (2003), we have sup ξ∗∈Λ |K3n(ξ)|= op(1).”
Asymptotic inference for a nonstationary double AR(1) model (2008, methodology): “This self-weighted estimator was proposed by Ling (2005, 2007b).”
Approximate self-weighted LAD estimation of discretely observed ergodic Ornstein-Uhlenbeck processes (2010, unknown): “On the other hand, Ling [11] introduced a self-weighted LAD (SLAD) contrast function for inﬁnite-variance autoregressive models, which entails asymptotically normally distributed estimators at rate √ n .”
Self-weighted and local quasi-maximum likelihood estimators for ARMA-GARCH/IGARCH models (2007, methodology): “Ling, 2005 introduced a selfweighted LAD estimator.”
Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso (2007, background): “Therefore, over the past decade, much effort has been devoted to establish the √ n-consistency and asymptotic normality of various LAD estimators (Bassett and Koenker 1978; Pollard 1991; Bloomfield and Steiger 1983; Knight 1998; Peng and Yao 2003; Ling 2005), which left the important problem of robu”
Risk-parameter estimation in volatility models (2012, methodology): “…quantile regression, and see Xiao and Koenker (2009), Xiao and Wan (2010) for recent applications to linear GARCH models) and in the least-absolute deviations (LAD) time series literature (see Davis, Knight and Liu (1992), Davis and Dunsmuir (1997), Breidt, Davis and Trindade (2001), Ling (2005)).”
Optimal predictions of powers of conditionally heteroscedastic processes (2013, background): “…developed in the literature which are known to be relatively efficient in the case of non-Gaussian noise; see for instance Davis et al. (1992) and Ling (2005) for the study of M-estimators for auto-regressions with infinite variance and Mukherjee (2008) for the study of M-estimators for GARCH.”
Maximum likelihood estimation for α-stable autoregressive processes (2009, background): “Parameter estimation for causal, heavy-tailed AR processes has already been considered in the literature (Davis and Resnick [14], least squares estimators; Davis [11] and Davis, Knight and Liu [12], least absolute deviations and other M -estimators; Mikosch, Gadrich, Klüppelberg and Adler [27], Whit”
LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic Noises (2015, methodology): “Obviously, these weight functions satisfy Assumptions 3 and 4, see also Ling (2005) and Pan, Wang, and Yao (2007) for more choices of wt . Theoretically, how to choose the optimal weight function is still a challenging open question. In practice, we can estimate the tail index α by Hill’s estimators”