頁籤選單縮合
題 名 | How Well Do Selection Models Perform? Assessing the Acuracy of Art Auction Pre-Sale Estimates |
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作 者 | Yu, Binbing; Gastwirth, Joseph L.; | 書刊名 | Statistica Sinica |
卷 期 | 20:2 2010.04[民99.04] |
頁 次 | 頁837-852 |
分類號 | 319.5 |
關鍵詞 | Art auction; Forecast error; Nonignorable missing data; Selection model; |
語 文 | 英文(English) |
英文摘要 | Abstract: Art auction catalogs provide a pre-sale prediction interval for the price each item is expected to fetch. When the owner consigns art work to the auction house, a reserve price is agreed upon, which is not announced to the bidders. If the highest bid does not reach it, the item is brought in. Since only the prices of the sold items are published, analysts only have a biased sample to examine due to the selective sale process. Relying on the published data leads to underestimating the forecast error of the pre-sale estimates. However, we were able to obtain several art auction catalogs with the highest bids for the unsold items as well as those of the sold items. With these data we were able to evaluate the accuracy of the predictions of the sale prices or highest bids for all item obtained from the original Heckman selection model that assumed normal error distributions as well as those derived from an alternative model using the distribution, which yielded a noticeably better fit to several sets of auction data. The measures of prediction accuracy are of more than academic interest as they are used by auction participants to guide their bidding or selling strategy, and similar appraisals are accepted by the US Internal Revenue Services to justify the deductions for charitable contributions donors make on their tax returns. |
本系統中英文摘要資訊取自各篇刊載內容。