Research interest in auctions has grown steadily in nearly every business discipline because of the distinctive constructs and processes that the data analyses yield. Auction data present researchers with a particular challenge because of the presence of no sale items. Researchers have varied in their accommodation of no sale items and the analysis technique, which may lead to biases in the analyses. The first question is how should no sale items be treated? To address this, the type of data generated by auctions must be examined, since various methods handle no sale items differently. Additional questions are: What analysis technique is preferred for this type of data? What is the effect of using other analysis techniques? The results suggest that including no sale items improves estimation accuracy. Also, we found that using ordinary least squares with censored data provides results that are much better than using truncated data.
|State||Published - Mar 2017|