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Estimation for unequally spaced time series of counts with serially correlated random effects

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  • Omori, Yasuhiro

Abstract

A generalized model for time series count data with serially correlated random effects is introduced to establish robust inference procedures. Observations are taken at possibly unequally spaced time intervals and random effects are assumed to have a stationary ergodic continuous time first-order autoregressive process. This is a generalization of Zeger (Biometrika 75 (1988) 621) where observations are taken at equally spaced time intervals and random effects are assumed to follow a discrete autoregressive process. For reasons of robustness, the distributional forms of random effects is not specified. The central limit theorem is established to prove asymptotic normality of estimators.

Suggested Citation

  • Omori, Yasuhiro, 2003. "Estimation for unequally spaced time series of counts with serially correlated random effects," Statistics & Probability Letters, Elsevier, vol. 63(1), pages 1-12, May.
  • Handle: RePEc:eee:stapro:v:63:y:2003:i:1:p:1-12
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    References listed on IDEAS

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    1. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
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    Cited by:

    1. E. Andres Houseman & Brent Coull & James Shine, 2004. "A Nonstationary Negative Binomial Time Series with Time-Dependent Covariates: Enterococcus Counts in Boston Harbor," Harvard University Biostatistics Working Paper Series 1017, Berkeley Electronic Press.

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