Did you ever try extending it out to other methods of probability estimation other than the forms of regression? I have only skimmed your excellent article, but I think you are first calculating the average probabilities from a regression model and then minimizing the loss to calculate Harville corrections for place and show markets? Is that correct or am I missing something here? I guess I am curious if there has been any improvement on using regressions for combining the various initial odds as I don't really follow the literature anymore.
Yes! There have been big improvements since then but they are beyond the scope of the post. I just wanted to reproduce the calculations in the paper using PyTorch.
Bill Benter subsequently replaced the multinomial logit model with a multinomial probit model, which assumes Normally distributed errors rather than errors that follow the Laplace distribution.
Sorry - to be clear this is just re-running the model detailed in Bill Benter’s 1995 paper (he uses the time period 1986-1993) on more recent time periods (1996-2003, 2006-2013, 2016-2023) using PyTorch.
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Bill Benter subsequently replaced the multinomial logit model with a multinomial probit model, which assumes Normally distributed errors rather than errors that follow the Laplace distribution.