Asymptotic Theory of Statistical Inference for Time Series
By M. Taniguchi, Yoshihide Kakizawa. 2000
212 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/15ee835517f9e58a8071a15120b6bb8321218d7f
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In Management Science and Operations Research
In Statistical and Nonlinear Physics
In Statistics and Probability
- Gregory Rice
- A. C. Davison
- Lajos Horv'ath
- Konstantinos Fokianos
- Xanthi Pedeli
- Alexander Aue
- Claudia Kirch
- Piotr Kokoszka
Citations in the corpus
- Extensions of some classical methods in change point analysis (2014, background): “(4.1) In addition to Taniguchi and Kakizawa (2000) and Liu and Wu (2010), a discussion of Bartlett-type kernel estimators satisfying (4.1) is in Brockwell and Davis (1991).”
- Quantile coherency: A general measure for dependence between cyclical economic variables (2015, unknown): “ous orthogonal increment process fZ˝ j (!) : ˇ!ˇg, for every j2f1;:::;dgand ˝2[0;1], such that the Cramer representation IfX t;j q j(˝)g= Z ˇ ˇ eit!dZ˝ j (!) 7 holds [cf., e.g., Theorem 1.2.15 in Taniguchi and Kakizawa (2000)]. Note the fact that (X t;j) t2Z is strictly stationary and therefore (IfX”
- Asymptotic Properties of GARCH-X Processes (2015, unknown): “4 in Taniguchi and Kakizawa (2000)).”
- Contiguity of the Whittle measure for a Gaussian time series (2004, unknown): “Taniguchi (1987) and Taniguchi & Kakizawa (2000) provided results and references on higher-order efficiency of maximum likelihood estimators using (4) in autoregressive moving average models.”
- Dynamic Orthogonal Components for Multivariate Time Series (2011, unknown): “(10) Note that xt is strong mixing with geometric rate if there ex- ists constants K > 0 and a ∈ (0,1) such that sup A∈σ 〈xt,t≤0〉,B∈σ 〈xt,t>k〉 |P(A ∩ B) − P(A)P(B)| = φk ≤ Kak, in which φk is referred to as the mixing rate function (see Taniguchi and Kakizawa 2000).”
- Likelihood Estimation for the INAR(p) Model by Saddlepoint Approximation (2015, methodology): “Below we suggest a saddlepoint approximation to (2) (see, e.g., Butler 2007; Taniguchi and Kakizawa 2000; Davison 2003, chap.”