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Multivariate Count Data Models for Time Series Forecasting.

Yuliya Shapovalova1, Nalan Baştürk2, Michael Eichler2

  • 1Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 212, 6525 EC Nijmegen, The Netherlands.

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Summary
This summary is machine-generated.

This study compares two count data models: the log-linear multivariate conditional intensity model and the non-linear state-space model. Findings show each model has advantages for forecasting, depending on the situation.

Keywords:
INGACRCHbank failuresmultivariate count datastate-space modeltransactions

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Area of Science:

  • Statistics
  • Econometrics
  • Time Series Analysis

Background:

  • Count data modeling is challenging due to its unique characteristics.
  • Common approaches include observation-driven and parameter-driven models.
  • Existing models often struggle with specific data features.

Purpose of the Study:

  • To review and compare two distinct count data models: log-linear multivariate conditional intensity (integer-valued generalized autoregressive conditional heteroskedastic) and non-linear state-space models.
  • To evaluate their forecasting performance on simulated and real-world datasets.
  • To analyze model misspecification effects and discuss inference pros and cons.

Main Methods:

  • Comparative analysis of forecasting performance.
  • Utilized simulated data, including scenarios of model misspecification.
  • Applied two real-world datasets for empirical validation.

Main Results:

  • Both the log-linear multivariate conditional intensity model and the non-linear state-space model demonstrate strengths in different forecasting contexts.
  • Model misspecification impacts forecasting accuracy, highlighting the importance of appropriate model selection.
  • Detailed discussion provided on the advantages and disadvantages of inference for each model.

Conclusions:

  • Neither model universally outperforms the other; selection depends on specific data characteristics and forecasting objectives.
  • The study offers practical insights for researchers and practitioners in choosing appropriate count data models.
  • Understanding model limitations and inference complexities is crucial for reliable count data analysis.