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Efficient Method Of Moments Estimators For Integer Time Series Models

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  • Vance L. Martin
  • Andrew R. Tremayne
  • Robert C. Jung

Abstract

type="main" xml:id="jtsa12078-abs-0001"> The parameters of integer autoregressive models with Poisson, or negative binomial innovations can be estimated by maximum likelihood where the prediction error decomposition, together with convolution methods, is used to write down the likelihood function. When a moving average component is introduced this is not the case. To address this problem an efficient method of moment estimator is proposed where the estimated standard errors for the parameters are obtained using subsampling methods. The small sample properties of the estimator are investigated using Monte Carlo methods, while the approach is demonstrated using two well-known examples from the time series literature.

Suggested Citation

  • Vance L. Martin & Andrew R. Tremayne & Robert C. Jung, 2014. "Efficient Method Of Moments Estimators For Integer Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(6), pages 491-516, November.
    • Handle: RePEc:bla:jtsera:v:35:y:2014:i:6:p:491-516
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    References listed on IDEAS

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    5. Dungey Mardi & Martin Vance L. & Tang Chrismin & Tremayne Andrew, 2020. "A threshold mixed count time series model: estimation and application," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-18, April.

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