Log-linear Poisson autoregression

Fokianos, Konstantinos; Tjøstheim, Dag

doi:10.1016/j.jmva.2010.11.002

Cited by 186 publications

(188 citation statements)

References 23 publications

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“…Similarly, Ω in the following Theorem 4 is also positive definite, which was pointed out by Lemma A.3 in Fokianos and Tjøstheim (2011).…”

Section: Remarkmentioning

confidence: 69%

“…Although Theorem 1 and the following Theorem 4 have been given by Fokianos et al (2009) and Fokianos and Tjøstheim (2011), respectively, we list them here for convenience of reader. In addition, we want to emphasize that these two theorems can be proved directly, i.e., without using the perturbation method in their papers.…”

Section: The Linear Ingarch Modelmentioning

confidence: 99%

“…To address these two problems, Fokianos and Tjøstheim (2011) proposed the following log-linear Poisson INGARCH(1, 1) model…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Empirical likelihood for linear and log-linear INGARCH models

Zhu¹,

Wang²

2015

Journal of the Korean Statistical Society

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a b s t r a c tThe integer-valued GARCH model is a popular tool for modeling time series of counts. This paper develops empirical likelihood methods for the linear and log-linear integer-valued GARCH models, respectively. We consider three aspects of the empirical likelihood method: estimates, confidence regions and test. For both models, an empirical log-likelihood ratio statistic is derived and its asymptotic distribution is shown to be a chi-squared distribution. Simulation studies lead to superior performance of the empirical likelihood method compared with the conventional treatments in the literature. An application to a real data example is also provided.

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“…Similarly, Ω in the following Theorem 4 is also positive definite, which was pointed out by Lemma A.3 in Fokianos and Tjøstheim (2011).…”

Section: Remarkmentioning

confidence: 69%

Section: The Linear Ingarch Modelmentioning

confidence: 99%

See 1 more Smart Citation

Empirical likelihood for linear and log-linear INGARCH models

Zhu¹,

Wang²

2015

Journal of the Korean Statistical Society

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“…Nonetheless, one can include particular functions of lags of Y t as long as the process is stationary. Examples are the log-linear Poisson Autoregression of Fokianos and Tjøstheim (2011), where ln(1…”

Section: Testing Lack Of Autocorrelation On Dynamic Count Data Modelsmentioning

confidence: 99%

Testing for Uncorrelated Residuals in Dynamic Count Models With an Application to Corporate Bankruptcy

Sant’Anna

2017

Journal of Business & Economic Statistics

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This article proposes a new diagnostic test for dynamic count models, which is well suited for risk management. Our test proposal is of the Portmanteau-type test for lack of residual autocorrelation. Unlike previous proposals, the resulting test statistic is asymptotically pivotal when innovations are uncorrelated, but not necessarily iid nor a martingale difference. Moreover, the proposed test is able to detect local alternatives converging to the null at the parametric rate T −1/2 , with T the sample size.The finite sample performance of the test statistic is examined by means of a Monte Carlo experiment. Finally, using a dataset on U.S. corporate bankruptcies, we apply our test proposal to check if common risk models are correctly specified.

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“…Following Fokianos and Tjøstheim (2011), who develop ergodicity conditions for a subclass of the arising log-linear models, we set c = 1. Another choice for η which has received some attention is the identity, η = id, see e.g.…”

Section: Introductionmentioning

confidence: 99%

On Outliers and Interventions in Count Time Series following GLMs

Fried

Liboschik

Elsaied³

et al. 2014

AJS

Self Cite

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We discuss the analysis of count time series following generalised linear models in the presence of outliers and intervention effects. Different modifications of such models are formulated which allow to incorporate, detect and to a certain degree distinguish extraordinary events (interventions) of different types in count time series retrospectively. An outlook on extensions to the problem of robust parameter estimation, identification of the model orders by robust estimation of autocorrelations and partial autocorrelations, and online surveillance by sequential testing for outlyingness is provided.

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Log-linear Poisson autoregression

Cited by 186 publications

References 23 publications

Empirical likelihood for linear and log-linear INGARCH models

Empirical likelihood for linear and log-linear INGARCH models

Testing for Uncorrelated Residuals in Dynamic Count Models With an Application to Corporate Bankruptcy

On Outliers and Interventions in Count Time Series following GLMs

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