基于贝叶斯泊松INGARCH 模型分析温度对犯罪的因果检验

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Abstract

本研究在贝叶斯框架下构建泊松INGARCH 计数时间序列模型，以温度为外生变量，解析其与性侵犯、盗窃及毒品犯罪的动态因果机制。研究选用贝叶斯估计方法，因其在参数不确定性和时变特性分析中的优越性，可有效规避传统估计方法的局限性。通过分析了解到温度对性侵犯无显著影响，而对盗窃与毒品犯罪均呈显著负向冲击。本文还表明贝叶斯推断不仅能精确地描述受外生冲击的整数值时间序列动态特征，还可有效解决过度离散问题，通过参数估计的尖锐化后验分布提升模型泛化能力。本研究进一步加深了对温度与犯罪之间关系的认识，显示了贝叶斯方法在增强预测犯罪分析方面的能力，为犯罪预测系统的优化提供理论支撑。

Keywords

计数时间序列

泊松INGARCH

贝叶斯

因果检验

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