Judgmental extrapolation and the salience of change
Judgmental extrapolation and the salience of change
复制标题
判断外推和变化的显着性
DOI：
10.1002/for.3980090405
复制
发表时间：
1990
影响因子：
3.4
通讯作者：
Stephen J. Kraus
中科院分区：
文献类型：
--
作者：
Paul B. Andreassen;Stephen J. Kraus
人们可能经常使用类似于正式时间序列外推模型的认知程序进行预测。提出了一种基于指数平滑的判断外推模型，其中假设趋势参数的设置取决于连续变化的相对显着性。首先通过使用框架操作用指数级数测试显着性假设。正如预测的那样，将受试者的注意力集中在变化上可以带来更准确的预测。在两项投资模拟研究中，通过改变价格变化的统计特性进一步检验了显着性假设。正如预测的那样，当价格下跌时，受试者更有可能卖出，而当价格上涨时，受试者更有可能买入 (1) 随着类似变化的样本量增加； (2) 当变化的方差较低时； (3)当平均变化的绝对值较高时。讨论了可能影响判断预测过程的条件。
People may often forecast using cognitive procedures that resemble formal time-series extrapolation models. A model of judgmental extrapolation based on exponential smoothing is proposed in which the setting of the trend parameter is hypothesized to depend upon the relative salience of the successive changes. The salience hypothesis was first tested with exponential series by the use of a framing manipulation. As predicted, focusing the subjects' attention on the changes led to more accurate forecasts. In two investment simulation studies, the salience hypothesis was further examined by varying the statistical properties of the price changes. As predicted, subjects were more likely to sell as prices fell and to buy as prices rose (1) as the sample size of similar changes increased; (2) when the variance of the changes was low; and (3) when the absolute value of the mean change was high. Conditions that may influence judgmental forecasting processes are discussed.