A negative binomial integer‐valued GARCH model

By Fukang Zhu. 2011

229 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/736f5dc5078d6245506bd7ec10435163eede2758

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Sangyeol Lee
2. A. Ahmad
3. Cathy W. S. Chen
4. Konstantinos Fokianos
5. Christian Francq
6. V. Christou
7. Heng Liu
8. Richard A. Davis

In Finance

1. Sangyeol Lee
2. A. Ahmad
3. Christian Francq
4. Richard A. Davis
5. Cathy W. S. Chen
6. Heng Liu
7. V. Christou
8. Konstantinos Fokianos

In Statistics and Probability

1. Sangyeol Lee
2. Konstantinos Fokianos
3. Christian Francq
4. Cathy W. S. Chen
5. V. Christou
6. A. Ahmad
7. Richard A. Davis
8. Heng Liu

Citations in the corpus

• Theory and inference for a class of nonlinear models with application to time series of counts (2016, background): “Later Zhu (2010) and Zhu (2012b) considered negative binomial and zero-inflated Poisson-based models.”
• Infinitely Divisible Distributions in Integer‐Valued Garch Models (2015, unknown): “Replacing the distribution of deviates by other particular discrete ones, like negative binomial or generalized Poisson, analogous integer-valued GARCH models have been proposed and studied (Zhu, 2011, 2012a).”
• Poisson QMLE of Count Time Series Models (2016, background): “Many extensions and alternative conditional distributions have been proposed in the literature, for example, Heinen (2003), Zhu (2011, 2012a, 2012b, 2012c) and Christou and Fokianos (2014), but either the conditional distribution remains relatively constrained or it contains extra parameters that…”
• QUASI‐LIKELIHOOD INFERENCE FOR NEGATIVE BINOMIAL TIME SERIES MODELS (2013, methodology): “35 DOI: 10.1111/jtsa.12050 : 55–78 (2014) that avoids dependence on the parameter ; this is a crucial fact for applications since we only need to be concerned with the appropriate choice of the regression parameters (compare with the approach by Zhu, 2011).”
• Count Time Series Models (2012, methodology): “but we will insist on the simpler model (33) for ease of presentation (see Zhu (2011) for more).”
• Some recent progress in count time series (2011, background): “Note that other distributions may be used instead, for instance the negative binomial distribution, or the power series distribution [30].”
• Bayesian causality test for integer‐valued time series models with applications to climate and crime data (2017, background): “Following the idea of Berger and Delampady (1987), we express the posterior probability of H0 :γ1 =: : :=γK =0 as Pr.H0|Y, X/= { 1+ p.Y|X, M2/ p.Y|X, M1/ π.M2/ π.M1/ }−1 = ( 1+ 1 POR )−1 , .9/ where p.Y|X, M1/ denotes the marginal likelihood under the null hypothesis H0.”