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|Has title=Sequential Matching of Entrepreneurs to Accelerators and VCs (Academic Paper)Venture Capitalists|Has author=Ed Egan, Jeremy Fox,|Has RAs=Amir KazempourJames Chen,

 ==Files and Dbases== The primary dbase is '''vcdb2'''The main SQL script is E:\McNair\Projects\MatchingEntrepsToVC\DataWork\MatchingEntrepsV2.sql ==Summary== The objective of this project is to create a multi-stage matching model and estimate 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 a relevant theory paperby Jeremy ==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)