The attraction effect refers to a situation in which adding an inferior alternative to a choice set increases the share of the relatively dominating alternative. This research posits that decision task type affect the attraction effect. People usually seek justification for their decisions. In a selection (or rejection) task, they are more likely to emphasize the positive (or negative) features of each option. The addition of an asymmetrically dominated decoy to a binary set of options undoubtedly provides an extra positive feature for the dominant option, and therefore induces a greater attraction effect. Contrarily, in a rejection task condition, the decoy in the trinary set seems to be the worst option and would be eliminated first, and the remaining comparison is identical with the original binary condition. Therefore, the attraction effect may decrease. Besides, the decision task type interacts with the construal level to affect the attraction effect. Specifically, a low construal level, compared with a high construal level, dampens the attraction effect to a greater extent in a rejection task than in a selection task. Results from three experiments support the proposed hypotheses. 相似文献
Structural breaks in the level as well as in the volatility have often been exhibited in economic time series. In this paper, we propose new unit root tests when a time series has multiple shifts in its level and the corresponding volatility. The proposed tests are Lagrangian multiplier type tests based on the residual's marginal likelihood which is free from the nuisance mean parameters. The limiting null distributions of the proposed tests are the χ2distributions, and are affected not by the size and the location of breaks but only by the number of breaks.
We set the structural breaks under both the null and the alternative hypotheses to relieve a possible vagueness in interpreting test results in empirical work. The null hypothesis implies a unit root process with level shifts and the alternative connotes a stationary process with level shifts. The Monte Carlo simulation shows that our tests are locally more powerful than the OLSE-based tests, and that the powers of our tests, in a fixed time span, remain stable regardless the number of breaks. In our application, we employ the data which are analyzed by Perron (1990), and some results differ from those of Perron's (1990). 相似文献