信噪比函数的置信区间在经济学和金融学中的应用

Warisa Thangjai, S. Niwitpong
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引用次数: 0

摘要

目的 置信区间在经济学和金融学中起着至关重要的作用,它为未知参数提供了可信的取值范围以及相应的确定性水平。其应用范围包括经济预测、市场研究、金融预测、计量经济学分析、政策分析、财务报告、投资决策、信贷风险评估和消费者信心调查。信噪比(SNR)应用于经济学和金融学的各个领域,如经济预测、金融建模、市场分析和风险评估。高信噪比表示信号稳健可靠,可简化做出明智决策的过程。另一方面,低信噪比表示信号较弱,可能会被噪声掩盖,因此决策程序需要认真考虑这一点。本研究的重点是开发信噪比函数的置信区间,并探讨其在经济和金融领域的应用。对于信噪比,采用广义信噪比(GCI)、大样本和贝叶斯方法形成置信区间。信噪比之间的差异是通过广义可信区间法、大样本法、方差估计恢复法(MOVER)、参数自举法和贝叶斯法估计出来的。此外,还使用 GCI、调整 MOVER、计算和贝叶斯方法构建了共同信噪比的置信区间。通过蒙特卡罗模拟,使用覆盖概率和平均长度对这些置信区间的性能进行了评估。研究结果在 SNR 的覆盖概率和平均长度以及 SNR 之间的差异方面,GCI 方法的性能优于其他方法。因此,建议采用 GCI 方法来构建这些参数的置信区间。至于普通 SNR,贝叶斯方法的平均长度最短。因此,建议采用贝叶斯方法构建普通信噪比的置信区间。原创性/价值本研究提出了信噪比函数的置信区间,以评估经济和金融领域的信噪比估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Confidence intervals for functions of signal-to-noise ratio with application to economics and finance
PurposeConfidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.Design/methodology/approachThe construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.FindingsThe GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.Originality/valueThis research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.
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