Conceptualising an Initial Design Space for Guidance in Digital Physical Activity Support
本文定义并构建了数字身体活动支持中指导的概念设计空间，以解决指导概念模糊和应用不一致的问题。
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Beyond Aggregate VARs: A Bayesian Benchmark for HANK Models
本文提出了一种贝叶斯基准方法，用于同时建模宏观经济总量和微观层面数据的边际分布，以解决HANK模型缺乏经验基准的问题。
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Rewriting Ontology-Mediated Property Graph Queries into GQL
本文提出一种将基于本体的属性图查询重写为GQL的方法，解决了现有图查询语言缺乏导航和路径匹配功能的问题。
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Counting in Population Protocols on Graphs
研究了图上群体协议中计数问题，通过随机调度和状态转换方法，在无需节点标识符的情况下实现了近似计数，使用了约O(n)状态并在特定次数交互后稳定。
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Clustered Local Projections for Short and Ultra-Short Time Series -- A Hierarchical Bayesian Framework
Estimating the dynamic effects of economic shocks in very short time series is challenged by severe degrees-of-freedom constraints. This study proposes a Bayesian hierarchical local projection framework that, for the first time, integrates Bayesian hierarchical modeling with sparse finite mixture models to cluster time series in unbalanced panels according to the similarity of their impulse response profiles. By allowing short series to borrow strength from longer ones, the approach enhances estimation precision while effectively accommodating heterogeneous dynamics and data imbalance. Simulation exercises demonstrate substantial improvements in estimation performance under short-sample conditions. Empirical analysis further reveals pronounced heterogeneity in the responses of various price indicators to supply chain and oil price shocks.
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Conceptualising an Initial Design Space for Guidance in Digital Physical Activity Support
本文定义并构建了数字身体活动支持中指导的概念设计空间，以解决指导概念模糊和应用不一致的问题。
0 citationsRead paper
Beyond Aggregate VARs: A Bayesian Benchmark for HANK Models
本文提出了一种贝叶斯基准方法，用于同时建模宏观经济总量和微观层面数据的边际分布，以解决HANK模型缺乏经验基准的问题。
0 citationsRead paper
Rewriting Ontology-Mediated Property Graph Queries into GQL
本文提出一种将基于本体的属性图查询重写为GQL的方法，解决了现有图查询语言缺乏导航和路径匹配功能的问题。
0 citationsRead paper
Counting in Population Protocols on Graphs
研究了图上群体协议中计数问题，通过随机调度和状态转换方法，在无需节点标识符的情况下实现了近似计数，使用了约O(n)状态并在特定次数交互后稳定。
0 citationsRead paper
Clustered Local Projections for Short and Ultra-Short Time Series -- A Hierarchical Bayesian Framework
Estimating the dynamic effects of economic shocks in very short time series is challenged by severe degrees-of-freedom constraints. This study proposes a Bayesian hierarchical local projection framework that, for the first time, integrates Bayesian hierarchical modeling with sparse finite mixture models to cluster time series in unbalanced panels according to the similarity of their impulse response profiles. By allowing short series to borrow strength from longer ones, the approach enhances estimation precision while effectively accommodating heterogeneous dynamics and data imbalance. Simulation exercises demonstrate substantial improvements in estimation performance under short-sample conditions. Empirical analysis further reveals pronounced heterogeneity in the responses of various price indicators to supply chain and oil price shocks.
0 citationsRead paper