Difference between revisions of "Sequential Matching of Entrepreneurs to Accelerators and Venture Capitalists"
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See [[Fox (2008) - An Empirical Repeated Matching Game Applied to Market]] for a brief write up on Jeremy's theory paper | 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 (for now, call these men and women) 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) | ||
Revision as of 12:09, 5 June 2017
| Academic Paper | |
|---|---|
| Title | Matching Entrepreneurs to Accelerators and VCs (Academic Paper) |
| Author | Ed Egan, Jeremy Fox |
| RAs | Amir Kazempour |
| Status | In development |
| © edegan.com, 2016 | |
Summary
This paper describes a two-stage matching model and estimates this model using data on entrepreneurs that match to accelerators and (lead) venture capitalists. Once the model is estimated, we can enact various policy-relevant changes and estimate their effects. For example, we could eliminate non-profit accelerators, government-sponsored venture capitalists, or other participants.
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 (for now, call these men and women) 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)
