{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T05:32:22Z","timestamp":1781328742980,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Count data appears in many research fields and exhibits certain features that make modeling difficult. Most popular approaches to modeling count data can be classified into observation and parameter-driven models. In this paper, we review two models from these classes: the log-linear multivariate conditional intensity model (also referred to as an integer-valued generalized autoregressive conditional heteroskedastic model) and the non-linear state-space model for count data. We compare these models in terms of forecasting performance on simulated data and two real datasets. In simulations, we consider the case of model misspecification. We find that both models have advantages in different situations, and we discuss the pros and cons of inference for both models in detail.<\/jats:p>","DOI":"10.3390\/e23060718","type":"journal-article","created":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T23:59:55Z","timestamp":1623023995000},"page":"718","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multivariate Count Data Models for Time Series Forecasting"],"prefix":"10.3390","volume":"23","author":[{"given":"Yuliya","family":"Shapovalova","sequence":"first","affiliation":[{"name":"Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 212, 6525 EC Nijmegen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0313-3592","authenticated-orcid":false,"given":"Nalan","family":"Ba\u015ft\u00fcrk","sequence":"additional","affiliation":[{"name":"School of Business and Economics, Maastricht University, Tongersestraat 53, 6211 LM Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Eichler","sequence":"additional","affiliation":[{"name":"School of Business and Economics, Maastricht University, Tongersestraat 53, 6211 LM Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,5]]},"reference":[{"key":"ref_1","unstructured":"Schoenmaker, D. 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