These models find that a higher variance of buyers' valuations leads to higher ask prices, because the profitability of the high ask price strategy grows.
Most of the existing literature testing the predictions regarding search behavior and ask prices uses data from housing markets.
Some of the work also tests the hypothesis that the variance of buyers' willingness to pay will affect ask prices, offers, and time to sale.
This allows us to test whether higher ask prices actually deter buyers from making offers.
Given limited time and a pool of eight or ten computers of a given model type, the buyer might screen the computers and examine only those four with the lowest ask prices.
Our approach is therefore to test the predictions of the search model in this setting to assess empirically the role of ask prices and the overall relevance of search models in this online setting.
Before we move to the empirical tests, it is worth discussing some alternative explanations regarding the role of ask prices.
One might think that a natural alternative hypothesis would be that ask prices serve as a signal of unobserved quality.
However, even if ask prices served as signals of unobserved quality, the empirical predictions of the signaling model run counter to the predictions of the search model.
Another factor that could lead to an observed empirical relationship between ask prices and offers are omitted variables.
Our data consist of listings, ask prices, and offers for a sample of 301 Macintosh units.
Thus our sample contains listings and ask prices for the two months prior to and following the release of the report.