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

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  • Lu, Yang

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.

Suggested Citation

  • Lu, Yang, 2018. "Exact Likelihood Estimation and Probabilistic Forecasting in Higher-order INAR(p) Models," MPRA Paper 83682, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:83682
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    File URL: https://mpra.ub.uni-muenchen.de/83682/1/MPRA_paper_83682.pdf
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    Cited by:

    1. Baena-Mirabete, S. & Puig, P., 2020. "Computing probabilities of integer-valued random variables by recurrence relations," Statistics & Probability Letters, Elsevier, vol. 161(C).

    More about this item

    Keywords

    compound autoregressive process; probabilistic forecast of counts; matrix arithmetic.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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