Predicting bid prices by using machine learning methods

Jong Min Kim, Hojin Jung

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    Abstract

    It is well-known that empirical analysis suffers from multicollinearity and high dimensionality. In particular, this is much more severe in an empirical study of itemized bids in highway procurement auctions. To overcome this obstacle, this article employs the regularized linear regression for the estimation of a more precise interval for project winning bids. The approach is put to the test using empirical data of highway procurement auctions in Vermont. In our empirical analysis, we first choose a set of crucial tasks that determine a bidder’s bid amounts by using the random forest variable selection method. Given the selected tasks, project bid forecasting is conducted. We compare our proposed methodology with the least square linear model based on the bias and the standard root mean square error of the bid estimates. There is evidence supporting that the suggested approach provides superior forecasts for an interval of winning bids over the competing model. As far as we know, this article is the first attempt to provide reference bids of highway construction contracts.

    Original languageEnglish (US)
    Pages (from-to)2011-2018
    Number of pages8
    JournalApplied Economics
    Volume51
    Issue number19
    DOIs
    StatePublished - Apr 21 2019

    Keywords

    • Auctions
    • bidding
    • forecasting
    • regularized linear regression

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