Difference between revisions of "Sequential Matching of Entrepreneurs to Accelerators and Venture Capitalists"
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|Has title=Sequential Matching of Entrepreneurs to Accelerators and Venture Capitalists | |Has title=Sequential Matching of Entrepreneurs to Accelerators and Venture Capitalists | ||
|Has author=Ed Egan, Jeremy Fox | |Has author=Ed Egan, Jeremy Fox | ||
| − | |Has RAs= | + | |Has RAs=James Chen, |
|Has paper status=In development | |Has paper status=In development | ||
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==Summary== | ==Summary== | ||
Revision as of 13:42, 1 December 2017
| Academic Paper | |
|---|---|
| Title | Sequential Matching of Entrepreneurs to Accelerators and Venture Capitalists |
| Author | Ed Egan, Jeremy Fox |
| RAs | James Chen |
| Status | In development |
| © edegan.com, 2016 | |
Contents
Summary
This paper describes a multi-stage matching model and estimates this model using data on entrepreneurs that match to accelerators and (lead) venture capitalists.
See Fox (2008) - An Empirical Repeated Matching Game Applied to Market for a brief write up on Jeremy's theory paper
Simple Outline of Model
As of now, the goal is to simulate a repeated matching model with dynamically optimizing agents. More specifically, there are two sides for a matching market with transferable utility (generically, call these men and women for now) with a continuum of agents, but a finite number of types. They participate in matches for T periods and receive utility that is a sum of a structural component (determined solely by their type and the type they are matched with) and a individual taste component (with some known distribution).
What distinguishes this model from a static matching model is that the agents have some probability of transitioning between types that is conditional on the match they make in the previous period (e.g., a man of low type might be more likely to change into a man of high type after being matched to a woman of high type). When making these matches, the agents take these transition probabilities into account when evaluating expected future utility. This adds a dynamic element to the model.
Work to do
- Code up three algorithms to simulate match(primal,IPFP,dual)
- Compare with R code from NYU
Work done so far
- Coded up primal and IPFP (may have errors)
Work to do in near term
- Compare with R code from NYU (using current solvers/optimizers, and with Gurobi)
