Munich Personal RePEc Archive
metadata only
Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model
This paper introduces and evaluates new models for time series count data. The Autoregressive Conditional Poisson model (ACP) makes it possible to deal with issues of discreteness, overdispersion (variance greater than the mean) and serial correlation. A fully parametric approach is taken and a marg
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Recent advancements in integer-valued autoregressive models for count data time series: A comprehensive review#5309
- Essays on copula-based and non-linear modeling in regression and time series problems / Sven Pappert ; Gutachter: Matei Demetrescu, Florian Ziel ; Betreuer: Antonia Arsova#1353919
- Clustering of Time Series Data: Measures, Methods, and Applications#492525
- A New Approach to Volatility Modeling: The Factorial Hidden Markov Volatility Model#1235709
- glmSTARMA -- An R-Package for fitting autoregressive spatio-temporal models following generalized linear models#1076920
Record · ID 1277844
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive