On Fisher’s dispersion test for integer-valued autoregressive Poisson models with applications

Lee, Sangyeol; Park, Siyun; Chen, Cathy W. S.

doi:10.1080/03610926.2016.1228970

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Note that hypotheses (7) refer to the conditional process distribution (given the past). Therefore, such tests as proposed by Lee et al (2017); Weiß et al (2017), which test for marginal overdispersion or zero inflation (with the null being a marginal Poisson distribution), are not reasonable in our setup. In Section 2, we develop a general approach for analyzing the conditional compounding structure of a CP-INARCH model.…”

Section: Introductionmentioning

confidence: 92%

Testing the compounding structure of the CP-INARCH model

Weiß

Gonçalves

Mendes-Lopes

2017

Metrika

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A statistical test to distinguish between a Poisson INARCH model and a Compound Poisson INARCH model is proposed, based on the form of the probability generating function of the compounding distribution of the conditional law of the model. For first-order autoregression, the normality of the test statistics' asymptotic distribution is established, either in the case where the model parameters are specified, or when such parameters are consistently estimated. As the test statistics' law involves the moments of inverse conditional means of the Compound Poisson INARCH process, the analysis of their existence and calculation is performed by two approaches. For higher-order autoregressions, we use a bootstrap implementation of the test. A simulation study illustrating the finite-sample performance of this test methodology in what concerns its size and power concludes the paper. Keywords Count-data time series • compound Poisson distribution • INGARCH model • diagnostic tests • inverse moments Mathematics Subject Classification (2000) 60J10 • 62M02 • 62M10

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Section: Introductionmentioning

confidence: 92%

Testing the compounding structure of the CP-INARCH model

Weiß

Gonçalves

Mendes-Lopes

2017

Metrika

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“…The data set is classified by local government area and offence type. This data set has been studied in many literatures, including Lee et al [ 9 ], Chen and Lee [ 38 , 39 ], Kim and Lee [ 24 ], and Lee et al [ 40 ]. To investigate the behavior of the MDPDE in the presence of outliers, we consider the data series of liquor offences (LO) and transport regulatory offences (TRO) in Botany Bay from January 1995 to December 2012, which has 216 observations in each series.…”

Section: Empirical Studiesmentioning

confidence: 99%

Robust Estimation for Bivariate Poisson INGARCH Models

Kim

Lee

Kim

2021

Entropy

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In the integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models, parameter estimation is conventionally based on the conditional maximum likelihood estimator (CMLE). However, because the CMLE is sensitive to outliers, we consider a robust estimation method for bivariate Poisson INGARCH models while using the minimum density power divergence estimator. We demonstrate the proposed estimator is consistent and asymptotically normal under certain regularity conditions. Monte Carlo simulations are conducted to evaluate the performance of the estimator in the presence of outliers. Finally, a real data analysis using monthly count series of crimes in New South Wales and an artificial data example are provided as an illustration.

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Residual-based CUSUM of squares test for Poisson integer-valued GARCH models

Lee

2019

Journal of Statistical Computation and Simulation

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On Fisher’s dispersion test for integer-valued autoregressive Poisson models with applications

Cited by 4 publications

References 19 publications

Testing the compounding structure of the CP-INARCH model

Testing the compounding structure of the CP-INARCH model

Robust Estimation for Bivariate Poisson INGARCH Models

Residual-based CUSUM of squares test for Poisson integer-valued GARCH models

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