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摘要翻译：
漂移扩散模型(DDM)是一种具有扩散（布朗）信号的序贯抽样模型，决策者积累证据直到过程达到停止边界，然后停止并选择与该边界对应的替代方案。该模型已广泛应用于心理学、神经经济学和神经科学中，用来解释在一系列二元选择决策问题中观察到的选择模式和反应时间。本文给出了具有一般边界的DDM的统计检验。我们首先证明了一个刻画定理：我们找到了选择概率满足的一个条件，当且仅当选择概率是由某些DDM生成的。此外，我们证明了漂移和边界是唯一识别的。然后，我们利用我们的条件非参数估计漂移和边界，并构造一个检验统计量。
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英文标题：
《Testing the Drift-Diffusion Model》
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作者：
Drew Fudenberg, Whitney K. Newey, Philipp Strack, Tomasz Strzalecki
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最新提交年份：
2019
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Economics 经济学
二级分类：Theoretical Economics 理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
The drift diffusion model (DDM) is a model of sequential sampling with diffusion (Brownian) signals, where the decision maker accumulates evidence until the process hits a stopping boundary, and then stops and chooses the alternative that corresponds to that boundary. This model has been widely used in psychology, neuroeconomics, and neuroscience to explain the observed patterns of choice and response times in a range of binary choice decision problems. This paper provides a statistical test for DDM's with general boundaries. We first prove a characterization theorem: we find a condition on choice probabilities that is satisfied if and only if the choice probabilities are generated by some DDM. Moreover, we show that the drift and the boundary are uniquely identified. We then use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic.
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PDF链接：
https://arxiv.org/pdf/1908.05824
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