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The Circumstance-Driven Bivariate Integer-Valued Autoregressive Model.

Huiqiao Wang1,2, Christian H Weiß1

  • 1Department of Mathematics and Statistics, Helmut Schmidt University, Holstenhofweg 85, 22043 Hamburg, Germany.

Entropy (Basel, Switzerland)
|February 23, 2024
PubMed
Summary
This summary is machine-generated.

A new circumstance-driven bivariate integer-valued autoregressive (CuBINAR) model handles non-stationary count data. This model captures complex dependencies and is validated for real-world sales count analysis.

Keywords:
CuBINAR modelcircumstance drivennon-stationarity

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Area of Science:

  • Statistics
  • Econometrics
  • Time Series Analysis

Background:

  • Count time series often exhibit non-stationarity and complex dependencies.
  • Existing models may not adequately capture joint dynamics and external influencing factors.

Purpose of the Study:

  • To propose a novel circumstance-driven bivariate integer-valued autoregressive (CuBINAR) model.
  • To address non-stationarity in bivariate count time series using a joint categorical sequence.
  • To enhance the model for low-count data and analyze cross-dependences.

Main Methods:

  • Development of the CuBINAR model incorporating a joint categorical sequence for state definition.
  • Derivation of key stochastic properties.
  • Parameter estimation using Yule-Walker and conditional maximum likelihood methods.
  • Consistency analysis and finite-sample performance evaluation via simulation.

Main Results:

  • The CuBINAR model effectively models non-stationary bivariate count time series.
  • Estimation methods demonstrate consistency.
  • Simulation studies confirm the model's finite-sample performance.
  • The model is successfully applied to real-world sales count data.

Conclusions:

  • The proposed CuBINAR model offers a flexible framework for analyzing non-stationary bivariate count data.
  • It provides a valuable tool for understanding count processes influenced by common circumstances.
  • The model shows practical utility in fields like sales forecasting and marketing analysis.