Estimation in conditional first order autoregression with discrete support

By Robert C. Jung, G. Ronning, A. Tremayne. 2005

88 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f8469a29cc58ffc4866db154d7f9224e3dbe2883

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Citations in the corpus

Efficient probabilistic forecasts for counts (2011, background): “It was further investigated by, among others, Du and Li (1991), Dion et al. (1995), Latour (1998), Ispany et al. (2003), Freeland and McCabe (2004), Jung et al. (2005), McCabe and Martin (2005), Silva and Oliveira (2005), Jung and Tremayne (2006), Zhu and Joe (2006), Neal and Subba Rao (2007), Bu…”
Binomial thinning models for integer time series (2006, methodology): “The ones that seemed to work best are given by Jung et al. (2005) in their expressions (A.5)–(A.7).”
Estimation in an Integer-Valued Autoregressive Process with Negative Binomial Marginals (NBINAR(1)) (2012, background): “( 2005 ) in modeling overdispersed monthly strike data in the United States.”
The Poisson INAR(1) CUSUM chart under overdispersion and estimation error (2011, background): “An extensive comparative study of properties and performance of these and different types of estimators is provided by Jung et al. (2005).”
The INARCH(1) Model for Overdispersed Time Series of Counts (2010, methodology): “We restrict to the period from January 1994 to December 2002 (108 observations), which was already investigated by Jung et al. (2005). A line plot of the data is shown in Fig.”
Asymptotic behavior of unstable INAR(p) processes (2009, methodology): “[28] analyzed the finite sample behavior of several estimators by a Monte Carlo study.”
Efficient Estimation of Autoregression Parameters and Innovation Distributions forSemiparametric Integer-Valued AR(p) Models (Revision of DP 2007-23) (2008, unknown): “Efficient estimation for semiparametric INAR(p) models 3 attractive, asymptotically efficient estimator of (θ, α), and Jung et al. (2005) analyzed, by a Monte Carlo study, the finite sample behavior of several estimators for the INAR(1) case. We consider a semiparametric model, where hardly any assu”
CUSUM Monitoring of First-Order Integer-Valued Autoregressive Processes of Poisson Counts (2009, methodology): “Once an INAR(1) model has been identified as being appropriate for the time series N1, . . . , NT , model estimation can be done by several approaches (for details see Al-Osh and Alzaid (1987); Jung et al. (2005)).”
Integer-valued autoregressive models for counts showing underdispersion (2013, background): “[16] (negative binomial innovations) and by Jazi et al.”