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相关概念视频

Variation01:19

Variation

6.8K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
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Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Hindsight Biases01:12

Hindsight Biases

3.4K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
3.4K
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Residual Plots01:07

Residual Plots

4.6K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
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相关实验视频

Updated: Jun 26, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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贝叶斯对回报可预测性的调和

Borys Koval1,2, Sylvia Frühwirth-Schnatter3, Leopold Sögner2,1

  • 1Vienna Graduate School of Finance, WU Vienna University of Economics and Business, 1020 Vienna, Austria.

Studies in nonlinear dynamics and econometrics
|May 8, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了贝叶斯的回报可预测性方法,使用稳定的向量自回归 (VAR) 模型. 贝叶斯方法的表现优于传统的估计方法,最近的财务数据显示回报可预测性的证据很弱.

关键词:
贝叶斯因子是一个贝叶斯因子.贝叶斯的控制函数方法是贝叶斯的控制函数方法.这就是VAR VAR VAR.返回可预测性的可预测性收缩的先验是以前的.

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科学领域:

  • 金融计量经济学 金融计量经济学
  • 贝叶斯统计学 贝叶斯统计学
  • 资产定价是指资产的定价.

背景情况:

  • 回报的可预测性是金融经济学的一个关键问题.
  • 像普通最小平方 (OLS) 和减少偏差估计器这样的现有方法都有局限性.
  • 矢量自回归 (VAR) 模型通常用于分析财务时间序列.

研究的目的:

  • 开发和评估一种新的贝叶斯方法来研究回报可预测性.
  • 将拟议的贝叶斯方法与OLS和降低偏差估计器的性能进行比较.
  • 用历史财务数据和各种预测变量来评估回报可预测性.

主要方法:

  • 在双变的VAR模型中开发一个关键参数的新型收缩先验.
  • 贝叶斯方法与OLS和Amihud和Hurvich (2004) 通过模拟的减少偏差估计器的比较.
  • 该方法应用于历史的CRSP价值加权收益率和股息价格比率以及替代预测因素 (Welch & Goyal,2008).

主要成果:

  • 模拟研究表明,贝叶斯方法在控制错误阳性和负值方面超过了减少偏差估计器.
  • 使用1926-2004年数据的经验分析不支持回归可预测性;最近的数据 (1953-2021) 显示可预测性很弱.
  • 替代预测变量也为回报可预测性提供了微弱的证据,而非样本预测证实了这一点.

结论:

  • 建议的贝叶斯方法为估计回报可预测性提供了一个强大的替代方案.
  • 对回报可预测性的证据对所使用的数据期和预测变量敏感.
  • 这些发现表明,近期股票市场的可预测性有限,尽管存在.