摘要翻译：
预测成本现在是经验经济学的前沿。我们提出了一个非常规的工具，用于基于记录事件的个别（微观）发展来随机预测未来的费用。考虑一个公司、企业、机构或国家，它拥有关于特定历史事件的知识。对于每一个事件，都有一系列相关的子事件：支付或损失随着时间的推移而蔓延，所有这些都导致了一个无限随机的过程。然而，问题是一些已经发生的事件不一定要报告。其目的在于预测来自已报告、已发生但未报告以及尚未发生的事件的未来子事件流。我们的方法在定量风险评估方面得到了说明，但它也可以应用于其他领域，如创业、流行病、战争损害、广告和商业、数字支付或药物处方。作为一个理论贡献，无限随机过程的推理得到了发展。特别地，使用了以非齐次Poisson过程为标记的非齐次Poisson过程，它包括例如Cox过程作为特例。
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英文标题：
《Infinitely Stochastic Micro Forecasting》
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作者：
Mat\'u\v{s} Maciak, Ostap Okhrin and Michal Pe\v{s}ta
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  Forecasting costs is now a front burner in empirical economics. We propose an unconventional tool for stochastic prediction of future expenses based on the individual (micro) developments of recorded events. Consider a firm, enterprise, institution, or state, which possesses knowledge about particular historical events. For each event, there is a series of several related subevents: payments or losses spread over time, which all leads to an infinitely stochastic process at the end. Nevertheless, the issue is that some already occurred events do not have to be necessarily reported. The aim lies in forecasting future subevent flows coming from already reported, occurred but not reported, and yet not occurred events. Our methodology is illustrated on quantitative risk assessment, however, it can be applied to other areas such as startups, epidemics, war damages, advertising and commercials, digital payments, or drug prescription as manifested in the paper. As a theoretical contribution, inference for infinitely stochastic processes is developed. In particular, a non-homogeneous Poisson process with non-homogeneous Poisson processes as marks is used, which includes for instance the Cox process as a special case.
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PDF链接：
https://arxiv.org/pdf/1908.10636