发表机构
Zicklin School of Business, Baruch College, City University of New York; Faculty of Social Sciences, University of Macau（纽约市立大学巴鲁克学院齐克林商学院; 澳门大学社会科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出藤蔓 Copula VAR 方法，通过调和加权创新矩处理历史估计误差，实现联合事件预测的渐近有效推断，并证明终端边际不确定性主导附加协方差项，改善尾部覆盖。
AI 中文摘要
联合事件预测通常将基于过去预测误差的依赖估计与新估计的边际分布相结合。当每个历史误差保留其发布时可用的边际拟合时，推断必须考虑重叠的估计误差序列。我们在具有正态创新边际和固定、正确指定的高斯或正克莱顿藤蔓的稳定藤蔓 Copula VAR 中，推导了它们与终端预测估计的联合影响。截距恒等式和稳定的 VAR 滤波器将历史修正简化为调和加权的创新矩，而终端斜率不确定性仍然存在。由此产生的协方差估计器为在已实现预测状态下的固定单侧事件概率提供了渐近有效的重复样本区间。在高斯子模型中，相对于在相同观测值上重新拟合边际，保留已发布的变换增加了一个正半定协方差项。蒙特卡洛模拟表明，终端边际不确定性在数量上比这一附加项更重要，并且 logit 区间在所研究的设计中改善了左尾覆盖率。一项针对美国宏观经济发布的实时预测应用展示了边际估计如何对联合收缩预测概率的不确定性做出贡献，并指出了平稳边际模型的局限性。
英文摘要
Joint-event forecasts often combine a dependence estimate based on past forecast errors with newly estimated marginal distributions. When each historical error retains the marginal fit available at its issue date, inference must account for an overlapping sequence of estimation errors. We derive their joint influence with the terminal forecast estimates in a stable Vine Copula VAR with normal innovation margins and a fixed, correctly specified Gaussian or positive Clayton vine. An intercept identity and the stable VAR filter reduce the historical correction to harmonically weighted innovation moments, while terminal slope uncertainty remains. The resulting covariance estimator gives asymptotically valid repeated-sample intervals for fixed one-sided event probabilities at the realized forecast state. In the Gaussian submodel, retaining issued transforms adds a positive semidefinite covariance term relative to refitting margins on the same observations. Monte Carlo simulations show that terminal-margin uncertainty is quantitatively more important than this additional term and that logit intervals improve lower-tail coverage in the designs studied. A real-time forecasting application to U.S. macroeconomic releases shows how marginal estimation contributes to uncertainty in predicted probabilities of joint contractions and identifies limitations of the stationary marginal model.
Comments55 pages, 1 figure, 4 tables
