Does Greater Amount of Information Always Bolster Attitudinal Resistance?

Previous research suggests that attitudinal resistance to information that challenges a prior evaluation increases with the amount of information underlying the prior evaluation. We revisit this proposition in a context in which a set of important claims about a target brand are presented either alone—a lower amount of isolated information—or along with other favorable, but less important claims—a higher amount of embedded information. Results from two experiments show that when the challenge occurs immediately after the initial evaluation, a greater amount of embedded initial information does produce greater attitudinal resistance. However, when the challenge occurs after a delay, a lesser amount of isolated information produces greater attitudinal resistance. The findings qualify previous assumptions about the role of prior information in attitudinal resistance, and support a constructive view of judgment revision and attitudinal resistance.     

无需过度忧虑通货膨胀

远期均衡通胀尚未对大宗商品价格回落做出回应,而且实际上均衡通胀比5月份石油价格处在类似水平时更高了。我们建议远期均衡通胀下降仓位。石油价格已从7月初的140美元/桶的高点急剧回落至117美元/桶,当前已经回到5月初的水平。不出所料地,全球均衡通胀从7月初高点急剧下降,     

Confidence interval for the total advertising impact and its mean duration under Koyck models

The estimates of total advertising impact and of its mean duration in distributed lag models are important in budgeting and planning decisions. The typical approach to assessing the short and long term impact of advertising is to perform a sequential test, one on the short term coefficient and one on the lag parameter. However, estimate of the effect of advertising on sales involves the ratio of estimated regression parameters. The estimates of these parameters are known to be correlated. Generation of confidence intervals on such key estimates is possible, using information that is typically discarded. This paper develops and illustrates a method for developing these confidence intervals which specifically accounts for the correlation between the standard regression estimates. Examples are used to illustrate that the conclusion from standard sequential testing procedures can be erroneous. An empirical application then demonstrates the application of the suggested procedure. Finally, we provide insights on the length of this interval as the sample size and other characteristics are varied.