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Quasi-likelihood inference for negative binomial time series models

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Quasi-likelihood inference for negative binomial time series models. / Christou, V.; Fokianos, K.
In: Journal of Time Series Analysis, Vol. 35, No. 1, 01.2014, p. 55-78.

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

Harvard

Christou, V & Fokianos, K 2014, 'Quasi-likelihood inference for negative binomial time series models', Journal of Time Series Analysis, vol. 35, no. 1, pp. 55-78. https://doi.org/10.1111/jtsa.12050

APA

Christou, V., & Fokianos, K. (2014). Quasi-likelihood inference for negative binomial time series models. Journal of Time Series Analysis, 35(1), 55-78. https://doi.org/10.1111/jtsa.12050

Vancouver

Christou V, Fokianos K. Quasi-likelihood inference for negative binomial time series models. Journal of Time Series Analysis. 2014 Jan;35(1):55-78. Epub 2013 Dec 16. doi: 10.1111/jtsa.12050

Author

Christou, V. ; Fokianos, K. / Quasi-likelihood inference for negative binomial time series models. In: Journal of Time Series Analysis. 2014 ; Vol. 35, No. 1. pp. 55-78.

Bibtex

@article{6c86faa2d6a043ee939712584986a902,
title = "Quasi-likelihood inference for negative binomial time series models",
abstract = "We study inference and diagnostics for count time series regression models that include a feedback mechanism. In particular, we are interested in negative binomial processes for count time series. We study probabilistic properties and quasi‐likelihood estimation for this class of processes. We show that the resulting estimators are consistent and asymptotically normally distributed. These facts enable us to construct probability integral transformation plots for assessing any assumed distributional assumptions. The key observation in developing the theory is a mean parameterized form of the negative binomial distribution. For transactions data, it is seen that the negative binomial distribution offers a better fit than the Poisson distribution. This is an immediate consequence of the fact that transactions can be represented as a collection of individual activities that correspond to different trading strategies.",
author = "V. Christou and K. Fokianos",
year = "2014",
month = jan,
doi = "10.1111/jtsa.12050",
language = "English",
volume = "35",
pages = "55--78",
journal = "Journal of Time Series Analysis",
issn = "0143-9782",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - Quasi-likelihood inference for negative binomial time series models

AU - Christou, V.

AU - Fokianos, K.

PY - 2014/1

Y1 - 2014/1

N2 - We study inference and diagnostics for count time series regression models that include a feedback mechanism. In particular, we are interested in negative binomial processes for count time series. We study probabilistic properties and quasi‐likelihood estimation for this class of processes. We show that the resulting estimators are consistent and asymptotically normally distributed. These facts enable us to construct probability integral transformation plots for assessing any assumed distributional assumptions. The key observation in developing the theory is a mean parameterized form of the negative binomial distribution. For transactions data, it is seen that the negative binomial distribution offers a better fit than the Poisson distribution. This is an immediate consequence of the fact that transactions can be represented as a collection of individual activities that correspond to different trading strategies.

AB - We study inference and diagnostics for count time series regression models that include a feedback mechanism. In particular, we are interested in negative binomial processes for count time series. We study probabilistic properties and quasi‐likelihood estimation for this class of processes. We show that the resulting estimators are consistent and asymptotically normally distributed. These facts enable us to construct probability integral transformation plots for assessing any assumed distributional assumptions. The key observation in developing the theory is a mean parameterized form of the negative binomial distribution. For transactions data, it is seen that the negative binomial distribution offers a better fit than the Poisson distribution. This is an immediate consequence of the fact that transactions can be represented as a collection of individual activities that correspond to different trading strategies.

U2 - 10.1111/jtsa.12050

DO - 10.1111/jtsa.12050

M3 - Journal article

VL - 35

SP - 55

EP - 78

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

SN - 0143-9782

IS - 1

ER -