Exact Likelihood Estimation and Probabilistic Forecasting in Higher-order INAR(p) Models

Yang Lu

MPRA Paper from University Library of Munich, Germany

Abstract: The computation of the likelihood function and the term structure of probabilistic forecasts in higher-order INAR(p) models are qualified numerically intractable and the literature has considered various approximations. Using the notion of compound autoregressive process, we propose an exact and fast algorithm for both quantities. We find that existing approximation schemes induce significant errors for forecasting.

Keywords: compound autoregressive process; probabilistic forecast of counts; matrix arithmetic. (search for similar items in EconPapers)
JEL-codes: C22 C25 (search for similar items in EconPapers)
Date: 2018-01-01
New Economics Papers: this item is included in nep-ecm, nep-edu, nep-ets, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:83682

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