一荚两豆作为 VARMAX 特例的贴现模型

IF 2.2 4区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Mathematical Psychology Pub Date : 2024-04-11 DOI:10.1016/j.jmp.2024.102856
Niels Vanhasbroeck, Tim Loossens, Francis Tuerlinckx
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引用次数: 0

摘要

在本文中,我们在两种研究影响动态的动态建模方法之间建立了一种形式上的联系。更具体地说,我们表明指数贴现模型可以改写为 VARMAX 的一种特定情况,从而揭示了这两种模型的基本相似性和假设。这一推导对研究有一些重要影响。首先,它允许在研究中使用贴现模型的研究人员使用在更广泛的时间序列文献中建立的工具来评估其模型的适用性。其次,它揭示了贴现模型对其参数的一些隐含限制,从而为这些模型的实证测试和验证奠定了基础。这些限制之一涉及影响动力学文献中经常假设的指数型贴现函数。作为一种替代方案,我们简要介绍了准双曲贴现函数。
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Two peas in a pod: Discounting models as a special case of the VARMAX

In this paper, we establish a formal connection between two dynamic modeling approaches that are often taken to study affect dynamics. More specifically, we show that the exponential discounting model can be rewritten to a specific case of the VARMAX, thereby shedding light on the underlying similarities and assumptions of the two models. This derivation has some important consequences for research. First, it allows researchers who use discounting models in their studies to use the tools established within the broader time series literature to evaluate the applicability of their models. Second, it lays bare some of the implicit restrictions discounting models put on their parameters and, therefore, provides a foundation for empirical testing and validation of these models. One of these restrictions concerns the exponential shape of the discounting function that is often assumed in the affect dynamical literature. As an alternative, we briefly introduce the quasi-hyperbolic discounting function.

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来源期刊
Journal of Mathematical Psychology
Journal of Mathematical Psychology 医学-数学跨学科应用
CiteScore
3.70
自引率
11.10%
发文量
37
审稿时长
20.2 weeks
期刊介绍: The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome. Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation. The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology. Research Areas include: • Models for sensation and perception, learning, memory and thinking • Fundamental measurement and scaling • Decision making • Neural modeling and networks • Psychophysics and signal detection • Neuropsychological theories • Psycholinguistics • Motivational dynamics • Animal behavior • Psychometric theory
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