WeChat Mini Program
Old Version Features

An Investigation into the Uncertainty Revision Process of Professional Forecasters

Journal of Economic Dynamics and Control(2025)SCI 3区

ICMA Centre

Cited 0|Views1
Abstract
Following Manzan (2021), this paper examines how professional forecasters revise their fixed-event uncertainty (variance) forecasts and tests the Bayesian learning prediction that variance forecasts should decrease as the horizon shortens. We show that Manzan's (2021) use of first moment “efficiency” tests are not applicable to studying revisions of variance forecasts. Instead, we employ monotonicity tests developed by Patton and Timmermann (2012) in our first known application of these tests to second moments of survey expectations. We find strong evidence that the variance forecasts are consistent with the Bayesian learning prediction of declining monotonicity.
More
Translated text
Key words
Variance forecasts,survey expectations,Bayesian learning,monotonicity tests,inflation forecasts,GDP growth forecasts
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文研究了专业预测者如何调整其对固定事件不确定性(方差)的预测,并验证了贝叶斯学习预测理论,即随着预测期限的缩短,方差预测应该减少。研究创新性地应用了Patton和Timmermann (2012)发展的单调性测试来分析调查预期的二阶矩。

方法】:作者通过比较专业预测者的方差预测变化,使用了Patton和Timmermann的单调性测试来检验预测的准确性,这是该方法首次应用于二阶矩的调查预期分析。

实验】:实验通过分析专业预测者的数据,未具体提及数据集名称,但结果表明方差预测与贝叶斯学习预测的降低单调性一致。