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- W2890192960 abstract "This paper shows that the term structure of conditional, or predictive distributions allows for closed form expression in a large family of (possibly higher-order, or infinite order) thinning-based count processes such as INAR(p), INARCH(p), NBAR(p), and INGARCH(1,1). Very often, such predictive distributions are deemed intractable by the literature and the stateof-the-art approximation methods are either too time consuming, or not precise enough for estimation and forecasting purpose. In this paper, we propose a Taylor’s expansion algorithm for these predictive distributions, which is both exact and fast. We demonstrate its advantages with respect to existing methods in terms of the computational gain and/or precision, and illustrate the usefulness of the approach by a comparison between a variety of different model specifications using a polio count data." @default.
- W2890192960 created "2018-09-27" @default.
- W2890192960 creator A5064477629 @default.
- W2890192960 date "2018-01-01" @default.
- W2890192960 modified "2023-10-17" @default.
- W2890192960 title "Exact Likelihood Estimation and Forecasting in Higher-Order INAR(p) Models" @default.
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- W2890192960 doi "https://doi.org/10.2139/ssrn.3095219" @default.
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