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Printable Handouts
Navigable Slide Index
- Introduction
- Research objective
- Literature review
- Our integrated stochastic model approach
- Fundamental kernel of our model: latent WTB
- Data description
- Empirical findings: number of bidders per auction
- Empirical findings: number of bids per auction
- Empirical findings: bid time distribution
- Empirical findings: winning bid time
- Empirical findings: bid amount
- Empirical findings: "Buy-It-Now" (BIN) feature
- Conceptual model development
- Determining the latent competition set
- Latent bidders
- Latent bidders: data windowing approach
- Model development: rates for bid speed
- Model development: whether
- Model development: who
- Model development: when / who
- Model development: how much
- Empirical applications
- Auction design effect on WTB
- Seller reputation effect on WTB
- Bidder characteristics effect on WTB
- Bid characteristics effect on WTB
- Bidder experience effect on WTB
- Model results for bid speed
- Model results for probability of bidding BIN
- Number of latent competing bidders
- Model fit: MAPE
- Model fit: in-sample hit rate
- Observed vs. fitted number of bidders
- Observed vs. fitted bid time
- Observed vs. fitted bid amount
- Conclusions and future research
Topics Covered
- Our integrated stochastic model approach
- Data description
- Empirical findings
- Conceptual model development
- Latent bidders
- Model development
- Model results
- Model fit
Talk Citation
Bradlow, E.T. (2010, January 27). Bayesian stochastic dynamic models for internet auctions [Video file]. In The Business & Management Collection, Henry Stewart Talks. Retrieved November 14, 2024, from https://doi.org/10.69645/ZSWV7941.Export Citation (RIS)
Publication History
Bayesian stochastic dynamic models for internet auctions
Published on January 27, 2010
39 min