Count Time Series Models
By Konstantinos Fokianos. 2012
101 citations recorded by the source corpus.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1304d2e4a4a07bfecc613620624a9b83ccf57fa7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
- Konstantinos Fokianos
- Joseph Tadjuidje Kamgaing
- Jürgen Franke
- Claudia Kirch
- Richard A. Davis
- Harry Joe
- Vladas Pipiras
- Robert B. Lund
In Statistics and Probability
- Konstantinos Fokianos
- Claudia Kirch
- Joseph Tadjuidje Kamgaing
- Jürgen Franke
- Robert B. Lund
- Richard A. Davis
- Vladas Pipiras
- Harry Joe
Citations in the corpus
- Changepoints in times series of counts (2012, methodology): “For a review of the field, we refer to the recent survey by Fokianos (2011).”
- Poisson QMLE of Count Time Series Models (2016, unknown): “In view of Fokianos (2012), the INGARCH model admits a weak ARMA representation.”
- Count Time Series: A Methodological Review (2021, background): “It can be shown thatE[Xt] = d/(1 − ∑pj=1 bj) and that the autocovariance function of {Xt} has an identical form to that of a classical AR(p) model (Fokianos 2012).”
- QUASI‐LIKELIHOOD INFERENCE FOR NEGATIVE BINOMIAL TIME SERIES MODELS (2013, unknown): “When the errors are Poisson distributed, it can be shown that (Fokianos, 2012) EŒYt D EŒ t D d=.1 a1 b1/ and the autocovariance function of the process is given by CovŒYt ; YtCjhj D 8̂̂̂ < ˆ̂̂: 1 .a1 C b1/2 C b21 1 .a1 C b1/2 ; h D 0, b1.”