相关实验视频
Updated: Sep 18, 2025

05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.2K
政策不确定性对A股行业回报的时间效应 - 一种新的贝叶斯方法
Tianxing Zhu1, Jinyang Liu2, Daixing Zeng2
1Jinan University-University of Birmingham Joint Institute, Jinan University, Guangzhou, China.
PloS one
|June 25, 2025
概括
政策不确定性对A股行业的回报产生重大影响,网络效应解释了39%的回答. 经济和财政政策产生协同效应,而汇率和贸易政策具有竞争效应,影响系统性风险.
科学领域:
- 经济学 经济学 经济学
- 金融市场 金融市场
- 计量经济学 计量经济学
背景情况:
- 政策不确定性对A股行业的回报产生了时间变化的影响.
- 传统模型未能在政策不确定性分析中捕捉网络结构和溢出效应.
- 系统风险的准确识别受到未定量化的网络影响的阻碍.
研究的目的:
- 开发一个新的框架,将行业输出输出网络整合到政策不确定性分析中.
- 量化政策不确定性对A股行业回报的溢出效应.
- 分析五个政策不确定性维度对系统风险的不同影响.
主要方法:
- 将行业输入输出表作为矩阵嵌入到时间变量参数空间自回归模型 (TVP-SAR) 中.
- 在TVP-SAR模型中使用贝叶斯方法进行参数估计.
- 将政策不确定性分类为经济,财政,货币,汇率和贸易方面.
主要成果:
- 网络溢出效应约占A股行业对政策不确定性的回报反应的39%.
- 经济和财政政策的不确定性显示出积极的网络效应 (协同效应).
- 汇率和贸易政策的不确定性表现出负面的网络效应 (竞争或替代效应).
- 在财政和贸易政策不确定性下,系统性风险最高;除了贸易政策不确定性外,它在大多数群体中都会增加.
结论:
- 该研究引入了一个新的框架,用于分析相互关联的行业的政策不确定性.
- 网络结构显著调解了政策不确定性对金融市场的影响.
- 了解协同效应和竞争效应对于管理A股市场的系统风险至关重要.
更多相关视频
相关概念视频
Uncertainty: Confidence Intervals
4.8K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.8K
Uncertainty: Overview
999
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
999
Propagation of Uncertainty from Systematic Error
897
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
897
Propagation of Uncertainty from Random Error
1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
Parametric Survival Analysis: Weibull and Exponential Methods
634
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
634
Actuarial Approach
140
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
140

