Perez_00_Paperbiddistribution.pdf (634.71 kB)
On the distribution of bids for construction contract auctions
journal contributionposted on 2018-04-27, 09:02 authored by Pablo Ballesteros-Perez, Martin Skitmore
© 2016 Informa UK Limited, trading as Taylor & Francis Group. The statistical distribution representing bid values constitutes an essential part of many auction models and has involved a wide range of assumptions, including the Uniform, Normal, Lognormal and Weibull densities. From a modelling point of view, its goodness is defined by how well it enables the probability of a particular bid value to be estimated–a past bid for ex-post analysis and a future bid for ex-ante (forecasting) analysis. However, there is no agreement to date of what is the most appropriate form and empirical work is sparse. Twelve extant construction data-sets from four continents over different time periods are analysed in this paper for their fit to a variety of candidate statistical distributions assuming homogeneity of bidders (ID not known). The results show there is no one single fit-all distribution, but that the 3p Log-Normal, Fréchet/2p Log-Normal, Normal, Gamma and Gumbel generally rank the best ex-post and the 2p Log-Normal, Normal, Gamma and Gumbel the best ex-ante–with ex-ante having around three to four times worse fit than ex-post. Final comments focus on the results relating to the third and fourth standardized moments of the bids and a post hoc rationalization of the empirical outcome of the analysis.
- Architecture, Building and Civil Engineering
Published inConstruction Management and Economics
Pages106 - 121
CitationBALLESTEROS-PEREZ, P. and SKITMORE, M., 2016. On the distribution of bids for construction contract auctions. Construction Management and Economics, 35(3), pp. 106-121.
Publisher© Taylor and Francis
- AM (Accepted Manuscript)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
NotesThis is an Accepted Manuscript of an article published by Taylor & Francis in Construction Management and Economics on 19 Oct 2016, available online: https://doi.org/10.1080/01446193.2016.1247972http://www.tandfonline.com/[Article DOI].