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Nonlinear Poisson autoregression

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Nonlinear Poisson autoregression. / Fokianos, K.; Tjøstheim, D.
In: Annals of the Institute of Statistical Mathematics, Vol. 64, No. 6, 12.2012, p. 1205-1225.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fokianos, K & Tjøstheim, D 2012, 'Nonlinear Poisson autoregression', Annals of the Institute of Statistical Mathematics, vol. 64, no. 6, pp. 1205-1225. https://doi.org/10.1007/s10463-012-0351-3

APA

Fokianos, K., & Tjøstheim, D. (2012). Nonlinear Poisson autoregression. Annals of the Institute of Statistical Mathematics, 64(6), 1205-1225. https://doi.org/10.1007/s10463-012-0351-3

Vancouver

Fokianos K, Tjøstheim D. Nonlinear Poisson autoregression. Annals of the Institute of Statistical Mathematics. 2012 Dec;64(6):1205-1225. Epub 2012 Mar 13. doi: 10.1007/s10463-012-0351-3

Author

Fokianos, K. ; Tjøstheim, D. / Nonlinear Poisson autoregression. In: Annals of the Institute of Statistical Mathematics. 2012 ; Vol. 64, No. 6. pp. 1205-1225.

Bibtex

@article{b3cc29f2e1f24d34a6360e7487a21799,
title = "Nonlinear Poisson autoregression",
abstract = "We study statistical properties of a class of non-linear models for regression analysis of count time series. Under mild conditions, it is shown that a perturbed version of the model is geometrically ergodic and possesses moments of any order. This result turns out to be instrumental on deriving large sample properties of the maximum likelihood estimators of the regression parameters. The theory is illustrated with examples.",
keywords = "Geometric ergodicity, Link function , Maximum likelihood estimation, Perturbation , Smooth transition models ",
author = "K. Fokianos and D. Tj{\o}stheim",
year = "2012",
month = dec,
doi = "10.1007/s10463-012-0351-3",
language = "English",
volume = "64",
pages = "1205--1225",
journal = "Annals of the Institute of Statistical Mathematics",
issn = "0020-3157",
publisher = "Springer Netherlands",
number = "6",

}

RIS

TY - JOUR

T1 - Nonlinear Poisson autoregression

AU - Fokianos, K.

AU - Tjøstheim, D.

PY - 2012/12

Y1 - 2012/12

N2 - We study statistical properties of a class of non-linear models for regression analysis of count time series. Under mild conditions, it is shown that a perturbed version of the model is geometrically ergodic and possesses moments of any order. This result turns out to be instrumental on deriving large sample properties of the maximum likelihood estimators of the regression parameters. The theory is illustrated with examples.

AB - We study statistical properties of a class of non-linear models for regression analysis of count time series. Under mild conditions, it is shown that a perturbed version of the model is geometrically ergodic and possesses moments of any order. This result turns out to be instrumental on deriving large sample properties of the maximum likelihood estimators of the regression parameters. The theory is illustrated with examples.

KW - Geometric ergodicity

KW - Link function

KW - Maximum likelihood estimation

KW - Perturbation

KW - Smooth transition models

U2 - 10.1007/s10463-012-0351-3

DO - 10.1007/s10463-012-0351-3

M3 - Journal article

VL - 64

SP - 1205

EP - 1225

JO - Annals of the Institute of Statistical Mathematics

JF - Annals of the Institute of Statistical Mathematics

SN - 0020-3157

IS - 6

ER -