Analysis of low count time series data by poisson autoregression

Freeland, R. Keith; McCabe, Brendan

doi:10.1111/j.1467-9892.2004.01885.x

Cited by 137 publications

(83 citation statements)

References 16 publications

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“…Applications of INAR(p) processes in the medical sciences can be found in, for example, Franke and Seligmann (1993) and Cardinal et al (1999); applications to economics in, for example, Böckenholt (1999), Berglund and Brännäs (2001), Brännäs and Hellström (2001), Rudholm (2001), Böckenholt (2003), Brännäs and Shahiduzzaman (2004), Freeland and McCabe (2004), and Gourieroux and Jasiak (2004).…”

Section: Here Z (Ti) Jmentioning

confidence: 99%

Local Asymptotic Normality and Efficient Estimation for INAR(p) Models

Drost

Akker²,

Werker

2006

SSRN Journal

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Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integervalued phenomena that evolve in time. The distribution of an INAR(p) process is determined by two parameters: a vector of survival probabilities and a probability distribution on the nonnegative integers, called an immigration or innovation distribution. This paper provides an efficient estimator of the parameters, and in particular, shows that the INAR(p) model has the Local Asymptotic Normality property.

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Section: Here Z (Ti) Jmentioning

confidence: 99%

Local Asymptotic Normality and Efficient Estimation for INAR(p) Models

Drost

Akker²,

Werker

2006

SSRN Journal

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“…This is one of the main reasons why this model is used frequently (McKenzie (1988), Freeland and McCabe (2004), Sutradhar and Bari (2007)) as compared to other such as moving average order 1 (MA(1)) or equi-correlations type models. But, because of the complexity of the likelihood approach, it is of interest to seek for alternative approaches for the estimation of the parameters of the dynamic model (1.4).…”

Section: Introductionmentioning

confidence: 99%

GMM versus GQL inferences for panel count data

Jowaheer

Sutradhar

2009

Statistics & Probability Letters

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SUMMARYIt is well known that the likelihood inferences in dynamic mixed models for count data is extremely complicated. In this paper, we, first, develop a generalized method of moments (GMM) approach for the estimation of the parameters of such models. We then consider an alternative generalized quasi-likelihood (GQL) approach.The relative efficiency of the GQL approach to the GMM approach is examined by comparing the asymptotic variances of the GQL estimates of the parameters to the corresponding asymptotic variances of the GMM estimates.

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“…While theoretical properties of INAR models with Poisson innovations have been extensively studied in the literature (see, for instance, Freeland and McCabe (2004a), , and the references therein), relatively few contributions discuss the development of methods for INAR models with innovations distributed differently from the Poisson.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Forecasting in INAR(P) Models Using Sieve Bootstrap

Bisaglia

Gerolimetto

2018

SSRN Journal

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In this paper we analyse some bootstrap techniques to make inference in INAR(p) models. First of all, via Monte Carlo experiments we compare the performances of these methods when estimating the thinning parameters in INAR(p) models. We state the superiority of sieve bootstrap approaches on block bootstrap in terms of low bias and Mean Square Error (MSE). Then we apply the sieve bootstrap methods to obtain coherent predictions and confidence intervals in order to avoid difficulty in deriving the distributional properties. Keywords INAR(p) models, estimation, forecast, bootstrap JEL Codes C22, C53 AbstractIn this paper we analyse some bootstrap techniques to make inference in INAR(p) models. First of all, via Monte Carlo experiments we compare the performances of these methods when estimating the thinning parameters in INAR(p) models. We state the superiority of sieve bootstrap approaches on block bootstrap in terms of low bias and Mean Square Error (MSE). Then we apply the sieve bootstrap methods to obtain coherent predictions and confidence intervals in order to avoid difficulty in deriving the distributional properties.

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Analysis of low count time series data by poisson autoregression

Cited by 137 publications

References 16 publications

Local Asymptotic Normality and Efficient Estimation for INAR(p) Models

Local Asymptotic Normality and Efficient Estimation for INAR(p) Models

GMM versus GQL inferences for panel count data

Estimation and Forecasting in INAR(P) Models Using Sieve Bootstrap

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