大规模贝叶斯张量自回归模型
Large Bayesian Tensor Autoregressions
AI总结：
本文提出了一种贝叶斯张量自回归模型，用于分析大规模多维时间序列，特别是国际贸易动态，通过引入灵活的随机波动性和低秩分解方法来提高模型性能。
摘要：
近年来，多维经济数据集的可用性显著增加。例如，国家间商品的双边贸易值包含三个维度——进口国、出口国和商品，形成一个三阶张量时间序列。本文介绍了一种通用的贝叶斯张量自回归框架，用于分析大规模、多维时间序列的动力学，特别是不同国家和行业的国际贸易。与文献中标准的同方差假设不同，我们引入了灵活的随机波动性到张量自回归模型中。所提出的模型能够捕捉由于新冠疫情和近期战争爆发导致的时间变化波动性。为了解决计算挑战并减轻过拟合，我们开发了一种基于低秩Tucker分解和分层收缩先验的高效采样方法。此外，我们还提供了模型的因子解释，展示了Tucker分解如何将大规模分解贸易流投影到全球因素上。
英文摘要：
The availability of multidimensional economic datasets has grown significantly in recent years. An example is bilateral trade values across goods among countries, comprising three dimensions -- importing countries, exporting countries, and goods -- forming a third-order tensor time series. This paper introduces a general Bayesian tensor autoregressive framework to analyze the dynamics of large, multidimensional time series with a particular focus on international trade across different countries and sectors. Departing from the standard homoscedastic assumption in this literature, we incorporate flexible stochastic volatility into the tensor autoregressive models. The proposed models can capture time-varying volatility due to the COVID-19 pandemic and recent outbreaks of war. To address computational challenges and mitigate overfitting, we develop an efficient sampling method based on low-rank Tucker decomposition and hierarchical shrinkage priors. Additionally, we provide a factor interpretation of the model showing how the Tucker decomposition projects large-dimensional disaggregated trade flows onto global factors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
