Integer‐Valued GARCH Process

By R. Ferland, Alain Latour, D. Oraichi. 2006

518 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f08ea65c970b786c63de024f982527230ac237ae

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Citations in the corpus

Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models (2011, unknown): “(4) As shown by Ferland et al. (2006), the series {St} is covariance stationary as well as strictly stationary if ∑q i=1 αi+ ∑p j=1 βj < 1.”
Bayesian causality test for integer‐valued time series models with applications to climate and crime data (2017, methodology): “Under H0, model (2) becomes the INGARCH(1,1) model (see Ferland et al. (2006)).”
Poisson QMLE of Count Time Series Models (2016, methodology): “One of the most natural count time series models is the Poisson INGARCH model, which has been studied by Ferland et al. (2006).”
Interventions in log-linear Poisson autoregression (2012, methodology): “This time series refers to the monthly number of cases of campylobacterosis infections from January 1990 to the end of October 2000 in the north of the Province of Québec, Canada, see Ferland et al. (2006).”
QUASI‐LIKELIHOOD INFERENCE FOR NEGATIVE BINOMIAL TIME SERIES MODELS (2013, unknown): “The proof is analogous to Proposition 1 of Ferland et al. (2006), and therefore, it is omitted.”
Absolute regularity and ergodicity of Poisson count processes (2011, methodology): “In the case of a so-called INGARCH(1,1) process where λt is specified as λt = θ0 + θ1λt−1 + θ2Nt−1, Ferland, Latour and Oraichi [16] proved the stronger result that all moments of λt and Nt are finite.”
Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator (2012, methodology): “The log-linear Poisson autoregression model introduced by Fokianos and Tjøstheim (2011) and defined by Yk+1|X0:k, Y0:k ∼ P(e k) , Xk+1 = d+ aXk + b ln(1 + Yk+1) , where P(λ) is the Poisson distribution of parameter λ can also be written as in (1) by setting fy(x) = d+ ax+ b ln(1 + y).”
Theory and inference for a class of nonlinear models with application to time series of counts (2016, background): “Ferland, Latour, and Oraichi (2006) showed second moment stationarity through a sequence of approximating processes and Fokianos, Rahbek, and Tjøstheim (2009) established the consistency and asymptotic normality of the MLE by introducing a perturbed model.”
Self-Excited Threshold Poisson Autoregression (2013, background): “…also known as the Poisson integer-valued GARCH (INGARCH), has already received considerable study in the literature; see, for example, Ferland, Latour, and Oraichi (2006), Fokianos, Rahbek, and Tjøstheim (2009), Neumann (2011), Doukhan, Fokianos, and Tjøstheim (2012), Davis and…”
Changepoints in times series of counts (2012, background): “of animals in a given habitat (compare [15]) or of infections during an epidemic [8].”
Degenerate $$U$$- and $$V$$-statistics under ergodicity: asymptotics, bootstrap and applications in statistics (2013, background): “It follows from Proposition 6 of Ferland et al. (2006) that all moments of Y ∗t are bounded in probability.”
First‐order rounded integer‐valued autoregressive (RINAR(1)) process (2009, background): “Specifically, we would mention a recent integer-valued generalized autoregressive conditional heteroskedastic (GARCH) process introduced by Ferland et al. (2006) which again relies on the thinning operator .”
Estimation and testing for a Poisson autoregressive model (2011, methodology): “We note that the information matrix equality was given by Ferland et al. (2006) .”
Count Time Series Models (2012, methodology): “Several results for model (12) have been reported in the literature, see Rydberg and Shephard (2000), Streett (2000), Heinen (2003) and Ferland et al. (2006) who consider the following general model of order (p, q): Yt | FY,λt−1 ∼ Poisson(λt), λt = d + p ∑ i=1 aiλt−i + q ∑ j=1 bjYt−j, t ≥ max(p,…”