Limit theorems for regression models of time series of counts

Michel Blais, Brenda MacGibbon and Roch Roy

Statistics & Probability Letters, 2000, vol. 46, issue 2, 161-168

Abstract: Here we present some limit theorems for a general class of generalized linear models describing time series of counts Y1,...,Yn. Following Zeger (Biometrika 75 (1988) 621-629), we suppose that the serial correlation depends on an unobservable latent process {[var epsilon]t}. Assuming that the conditional distribution of Yt given [var epsilon]t belongs to the exponential family, that Y1[var epsilon]1,...,Yn[var epsilon]n are independent, and that the latent process satisfies a mixing condition, it is shown that the quasi-likelihood estimators of the regression coefficients are asymptotically normally distributed.

Keywords: Poisson; regression; Estimating; equations; Exponential; family; distribution; Asymptotic; distribution; Mixing (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (3)

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